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

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


# kernel path: runs/run_shard_7/inductor_cache/rn/crnvqrppxb3ocltksxzamskmx3mpvscqlqbanvhmgkmj5b53kfuf.py
# Topologically Sorted Source Nodes: [add, e], Original ATen: [aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   add => add
#   e => 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_7/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=5] = 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_7/inductor_cache/7p/c7pzocdj4z66742c4x73l2mmqga3yo74menkparuudjxrge7crrh.py
# Topologically Sorted Source Nodes: [add, e, zero_vec, attention, attention_1, add_1, e_1, attention_3, attention_4, add_2, e_2, attention_6, attention_7, add_3, e_3, attention_9, attention_10], Original ATen: [aten.add, aten.leaky_relu, aten.mul, aten.where, aten._softmax]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   attention => where_1
#   attention_1 => amax
#   attention_10 => amax_3
#   attention_3 => where_3
#   attention_4 => amax_1
#   attention_6 => where_5
#   attention_7 => amax_2
#   attention_9 => where_7
#   e => mul, where
#   e_1 => mul_2, where_2
#   e_2 => mul_4, where_4
#   e_3 => mul_6, where_6
#   zero_vec => 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, 4), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add, %mul), kwargs = {})
#   %full_default : [num_users=4] = 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})
#   %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 = {})
#   %add_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_5, %permute_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 4), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %add_1, %mul_2), kwargs = {})
#   %where_3 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where_2, %full_default), kwargs = {})
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where_3, [1], True), kwargs = {})
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_9, %permute_2), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 4), kwargs = {})
#   %where_4 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %add_2, %mul_4), kwargs = {})
#   %where_5 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where_4, %full_default), kwargs = {})
#   %amax_2 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where_5, [1], True), kwargs = {})
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_13, %permute_3), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, 4), kwargs = {})
#   %where_6 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_6, %add_3, %mul_6), kwargs = {})
#   %where_7 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where_6, %full_default), kwargs = {})
#   %amax_3 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where_7, [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: '*i1', 5: '*fp32', 6: '*fp32', 7: '*i1', 8: '*fp32', 9: '*fp32', 10: '*i1', 11: '*fp32', 12: '*fp32', 13: '*fp32', 14: '*fp32', 15: '*fp32', 16: '*fp32', 17: '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, 8, 9, 10, 11, 12, 13, 14, 15, 16), 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': 40, '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, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10, in_ptr11, in_ptr12, out_ptr0, out_ptr1, out_ptr2, out_ptr3, 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])
    tmp38 = tl.load(in_ptr4 + (4*x0), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr5 + (x0), xmask)
    tmp40 = tl.load(in_ptr6 + (0))
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK])
    tmp46 = tl.load(in_ptr4 + (1 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp47 = tl.load(in_ptr6 + (1))
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK])
    tmp54 = tl.load(in_ptr4 + (2 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp55 = tl.load(in_ptr6 + (2))
    tmp56 = tl.broadcast_to(tmp55, [XBLOCK])
    tmp62 = tl.load(in_ptr4 + (3 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp63 = tl.load(in_ptr6 + (3))
    tmp64 = tl.broadcast_to(tmp63, [XBLOCK])
    tmp70 = tl.load(in_ptr7 + (4*x0), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp71 = tl.load(in_ptr8 + (x0), xmask)
    tmp72 = tl.load(in_ptr9 + (0))
    tmp73 = tl.broadcast_to(tmp72, [XBLOCK])
    tmp78 = tl.load(in_ptr7 + (1 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp79 = tl.load(in_ptr9 + (1))
    tmp80 = tl.broadcast_to(tmp79, [XBLOCK])
    tmp86 = tl.load(in_ptr7 + (2 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp87 = tl.load(in_ptr9 + (2))
    tmp88 = tl.broadcast_to(tmp87, [XBLOCK])
    tmp94 = tl.load(in_ptr7 + (3 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp95 = tl.load(in_ptr9 + (3))
    tmp96 = tl.broadcast_to(tmp95, [XBLOCK])
    tmp102 = tl.load(in_ptr10 + (4*x0), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp103 = tl.load(in_ptr11 + (x0), xmask)
    tmp104 = tl.load(in_ptr12 + (0))
    tmp105 = tl.broadcast_to(tmp104, [XBLOCK])
    tmp110 = tl.load(in_ptr10 + (1 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp111 = tl.load(in_ptr12 + (1))
    tmp112 = tl.broadcast_to(tmp111, [XBLOCK])
    tmp118 = tl.load(in_ptr10 + (2 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp119 = tl.load(in_ptr12 + (2))
    tmp120 = tl.broadcast_to(tmp119, [XBLOCK])
    tmp126 = tl.load(in_ptr10 + (3 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp127 = tl.load(in_ptr12 + (3))
    tmp128 = tl.broadcast_to(tmp127, [XBLOCK])
    tmp5 = tmp2 + tmp4
    tmp6 = 4.0
    tmp7 = tmp5 * tmp6
    tmp8 = tl.where(tmp1, tmp5, tmp7)
    tmp9 = -8999999815811072.0
    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)
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42 * tmp6
    tmp44 = tl.where(tmp38, tmp42, tmp43)
    tmp45 = tl.where(tmp0, tmp44, tmp9)
    tmp49 = tmp39 + tmp48
    tmp50 = tmp49 * tmp6
    tmp51 = tl.where(tmp46, tmp49, tmp50)
    tmp52 = tl.where(tmp11, tmp51, tmp9)
    tmp53 = triton_helpers.maximum(tmp45, tmp52)
    tmp57 = tmp39 + tmp56
    tmp58 = tmp57 * tmp6
    tmp59 = tl.where(tmp54, tmp57, tmp58)
    tmp60 = tl.where(tmp20, tmp59, tmp9)
    tmp61 = triton_helpers.maximum(tmp53, tmp60)
    tmp65 = tmp39 + tmp64
    tmp66 = tmp65 * tmp6
    tmp67 = tl.where(tmp62, tmp65, tmp66)
    tmp68 = tl.where(tmp29, tmp67, tmp9)
    tmp69 = triton_helpers.maximum(tmp61, tmp68)
    tmp74 = tmp71 + tmp73
    tmp75 = tmp74 * tmp6
    tmp76 = tl.where(tmp70, tmp74, tmp75)
    tmp77 = tl.where(tmp0, tmp76, tmp9)
    tmp81 = tmp71 + tmp80
    tmp82 = tmp81 * tmp6
    tmp83 = tl.where(tmp78, tmp81, tmp82)
    tmp84 = tl.where(tmp11, tmp83, tmp9)
    tmp85 = triton_helpers.maximum(tmp77, tmp84)
    tmp89 = tmp71 + tmp88
    tmp90 = tmp89 * tmp6
    tmp91 = tl.where(tmp86, tmp89, tmp90)
    tmp92 = tl.where(tmp20, tmp91, tmp9)
    tmp93 = triton_helpers.maximum(tmp85, tmp92)
    tmp97 = tmp71 + tmp96
    tmp98 = tmp97 * tmp6
    tmp99 = tl.where(tmp94, tmp97, tmp98)
    tmp100 = tl.where(tmp29, tmp99, tmp9)
    tmp101 = triton_helpers.maximum(tmp93, tmp100)
    tmp106 = tmp103 + tmp105
    tmp107 = tmp106 * tmp6
    tmp108 = tl.where(tmp102, tmp106, tmp107)
    tmp109 = tl.where(tmp0, tmp108, tmp9)
    tmp113 = tmp103 + tmp112
    tmp114 = tmp113 * tmp6
    tmp115 = tl.where(tmp110, tmp113, tmp114)
    tmp116 = tl.where(tmp11, tmp115, tmp9)
    tmp117 = triton_helpers.maximum(tmp109, tmp116)
    tmp121 = tmp103 + tmp120
    tmp122 = tmp121 * tmp6
    tmp123 = tl.where(tmp118, tmp121, tmp122)
    tmp124 = tl.where(tmp20, tmp123, tmp9)
    tmp125 = triton_helpers.maximum(tmp117, tmp124)
    tmp129 = tmp103 + tmp128
    tmp130 = tmp129 * tmp6
    tmp131 = tl.where(tmp126, tmp129, tmp130)
    tmp132 = tl.where(tmp29, tmp131, tmp9)
    tmp133 = triton_helpers.maximum(tmp125, tmp132)
    tl.store(out_ptr0 + (x0), tmp37, xmask)
    tl.store(out_ptr1 + (x0), tmp69, xmask)
    tl.store(out_ptr2 + (x0), tmp101, xmask)
    tl.store(out_ptr3 + (x0), tmp133, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/si/csiextwu44e63m7askimz3girudboqtyp45f2wu2wmll5iovqchv.py
# Topologically Sorted Source Nodes: [add, e, zero_vec, attention, attention_1, add_1, e_1, attention_3, attention_4, add_2, e_2, attention_6, attention_7, add_3, e_3, attention_9, attention_10], Original ATen: [aten.add, aten.leaky_relu, aten.mul, aten.where, aten._softmax]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   attention => where_1
#   attention_1 => exp, sub
#   attention_10 => exp_3, sub_3
#   attention_3 => where_3
#   attention_4 => exp_1, sub_1
#   attention_6 => where_5
#   attention_7 => exp_2, sub_2
#   attention_9 => where_7
#   e => mul, where
#   e_1 => mul_2, where_2
#   e_2 => mul_4, where_4
#   e_3 => mul_6, where_6
#   zero_vec => 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, 4), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add, %mul), kwargs = {})
#   %full_default : [num_users=4] = 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})
#   %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 = {})
#   %add_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_5, %permute_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 4), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %add_1, %mul_2), kwargs = {})
#   %where_3 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where_2, %full_default), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_3, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_9, %permute_2), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 4), kwargs = {})
#   %where_4 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %add_2, %mul_4), kwargs = {})
#   %where_5 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where_4, %full_default), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_5, %amax_2), kwargs = {})
#   %exp_2 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_13, %permute_3), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, 4), kwargs = {})
#   %where_6 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_6, %add_3, %mul_6), kwargs = {})
#   %where_7 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where_6, %full_default), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_7, %amax_3), kwargs = {})
#   %exp_3 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), 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: '*i1', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*i1', 10: '*fp32', 11: '*fp32', 12: '*fp32', 13: '*i1', 14: '*fp32', 15: '*fp32', 16: '*fp32', 17: '*fp32', 18: '*fp32', 19: '*fp32', 20: '*fp32', 21: '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, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21), 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': 17, '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, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10, in_ptr11, in_ptr12, in_ptr13, in_ptr14, in_ptr15, in_ptr16, out_ptr0, out_ptr1, out_ptr2, out_ptr3, 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')
    tmp13 = tl.load(in_ptr5 + (x2), xmask).to(tl.int1)
    tmp14 = tl.load(in_ptr6 + (x1), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr7 + (x0), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr8 + (x1), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr9 + (x2), xmask).to(tl.int1)
    tmp24 = tl.load(in_ptr10 + (x1), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr11 + (x0), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr12 + (x1), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr13 + (x2), xmask).to(tl.int1)
    tmp34 = tl.load(in_ptr14 + (x1), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr15 + (x0), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr16 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp5 = 4.0
    tmp6 = tmp4 * tmp5
    tmp7 = tl.where(tmp1, tmp4, tmp6)
    tmp8 = -8999999815811072.0
    tmp9 = tl.where(tmp0, tmp7, tmp8)
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 * tmp5
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tl.where(tmp0, tmp18, tmp8)
    tmp21 = tmp19 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 * tmp5
    tmp28 = tl.where(tmp23, tmp26, tmp27)
    tmp29 = tl.where(tmp0, tmp28, tmp8)
    tmp31 = tmp29 - tmp30
    tmp32 = tl_math.exp(tmp31)
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 * tmp5
    tmp38 = tl.where(tmp33, tmp36, tmp37)
    tmp39 = tl.where(tmp0, tmp38, tmp8)
    tmp41 = tmp39 - tmp40
    tmp42 = tl_math.exp(tmp41)
    tl.store(out_ptr0 + (x2), tmp12, xmask)
    tl.store(out_ptr1 + (x2), tmp22, xmask)
    tl.store(out_ptr2 + (x2), tmp32, xmask)
    tl.store(out_ptr3 + (x2), tmp42, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rr/crrmj7r54x5uk325xkhuskxp4m5prz3fpx53yc2st4o5pwbhq32p.py
# Topologically Sorted Source Nodes: [attention_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention_1 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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')


# kernel path: runs/run_shard_7/inductor_cache/s2/cs2da62esedbp7cc5pkrkaaov26vqav3xrbyiivqo7k7yeqiw6pv.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_1 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%sigmoid, %sigmoid_1, %sigmoid_2, %sigmoid_3], 1), kwargs = {})
triton_poi_fused_cat_5 = async_compile.triton('triton_poi_fused_cat_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: '*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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_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_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp4, tmp6, tmp7)
    tmp9 = tmp0 >= tmp3
    tmp10 = tl.full([1], 8, tl.int64)
    tmp11 = tmp0 < tmp10
    tmp12 = tmp9 & tmp11
    tmp13 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp12, tmp14, tmp15)
    tmp17 = tmp0 >= tmp10
    tmp18 = tl.full([1], 12, tl.int64)
    tmp19 = tmp0 < tmp18
    tmp20 = tmp17 & tmp19
    tmp21 = tl.load(in_ptr2 + ((4*x1) + ((-8) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp22 = tl.sigmoid(tmp21)
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp25 = tmp0 >= tmp18
    tmp26 = tl.full([1], 16, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tl.load(in_ptr3 + ((4*x1) + ((-12) + x0)), tmp25 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = tl.sigmoid(tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp25, tmp29, tmp30)
    tmp32 = tl.where(tmp20, tmp24, tmp31)
    tmp33 = tl.where(tmp12, tmp16, tmp32)
    tmp34 = tl.where(tmp4, tmp8, tmp33)
    tl.store(out_ptr0 + (x2), tmp34, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 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, 1), (1, 1))
    assert_size_stride(primals_8, (4, 1), (1, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4, 1), (1, 1))
    assert_size_stride(primals_11, (4, 1), (1, 1))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4, 1), (1, 1))
    assert_size_stride(primals_14, (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_1, primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [f_1], 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: [f_2], 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: [add, e], 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
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, primals_6, out=buf9)
        del primals_6
        buf10 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [f_3], Original ATen: [aten.mm]
        extern_kernels.mm(buf9, primals_7, out=buf10)
        buf11 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [f_4], Original ATen: [aten.mm]
        extern_kernels.mm(buf9, primals_8, out=buf11)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [add_1, e_1], Original ATen: [aten.add, aten.leaky_relu]
        triton_poi_fused_add_leaky_relu_0.run(buf10, buf11, buf12, 16, grid=grid(16), stream=stream0)
        buf17 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_2], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, primals_9, out=buf17)
        del primals_9
        buf18 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [f_5], Original ATen: [aten.mm]
        extern_kernels.mm(buf17, primals_10, out=buf18)
        buf19 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [f_6], Original ATen: [aten.mm]
        extern_kernels.mm(buf17, primals_11, out=buf19)
        buf20 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [add_2, e_2], Original ATen: [aten.add, aten.leaky_relu]
        triton_poi_fused_add_leaky_relu_0.run(buf18, buf19, buf20, 16, grid=grid(16), stream=stream0)
        buf25 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_3], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, primals_12, out=buf25)
        del primals_12
        buf26 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [f_7], Original ATen: [aten.mm]
        extern_kernels.mm(buf25, primals_13, out=buf26)
        buf27 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [f_8], Original ATen: [aten.mm]
        extern_kernels.mm(buf25, primals_14, out=buf27)
        buf28 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [add_3, e_3], Original ATen: [aten.add, aten.leaky_relu]
        triton_poi_fused_add_leaky_relu_0.run(buf26, buf27, buf28, 16, grid=grid(16), stream=stream0)
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf21 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf29 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [add, e, zero_vec, attention, attention_1, add_1, e_1, attention_3, attention_4, add_2, e_2, attention_6, attention_7, add_3, e_3, attention_9, attention_10], 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, buf12, buf10, buf11, buf20, buf18, buf19, buf28, buf26, buf27, buf5, buf13, buf21, buf29, 4, grid=grid(4), stream=stream0)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, e, zero_vec, attention, attention_1, add_1, e_1, attention_3, attention_4, add_2, e_2, attention_6, attention_7, add_3, e_3, attention_9, attention_10], 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, buf12, buf10, buf11, buf13, buf20, buf18, buf19, buf21, buf28, buf26, buf27, buf29, buf6, buf14, buf22, buf30, 16, grid=grid(16), stream=stream0)
        del buf1
        del buf10
        del buf11
        del buf13
        del buf18
        del buf19
        del buf2
        del buf21
        del buf26
        del buf27
        del buf29
        del buf5
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_1], 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: [h_prime], Original ATen: [aten.mm]
        extern_kernels.mm(buf7, buf0, out=buf8)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_4], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf14, buf15, 16, grid=grid(16), stream=stream0)
        buf16 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [h_prime_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf15, buf9, out=buf16)
        buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_7], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf22, buf23, 16, grid=grid(16), stream=stream0)
        buf24 = buf22; del buf22  # reuse
        # Topologically Sorted Source Nodes: [h_prime_2], Original ATen: [aten.mm]
        extern_kernels.mm(buf23, buf17, out=buf24)
        buf31 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_10], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf30, buf31, 16, grid=grid(16), stream=stream0)
        buf32 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [h_prime_3], Original ATen: [aten.mm]
        extern_kernels.mm(buf31, buf25, out=buf32)
        buf33 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_5.run(buf8, buf16, buf24, buf32, buf33, 64, grid=grid(64), stream=stream0)
    return (buf33, buf3, buf4, buf7, buf8, buf12, buf15, buf16, buf20, buf23, buf24, buf28, buf31, buf32, reinterpret_tensor(buf25, (4, 4), (1, 4), 0), reinterpret_tensor(primals_14, (1, 4), (1, 1), 0), reinterpret_tensor(primals_13, (1, 4), (1, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), reinterpret_tensor(buf17, (4, 4), (1, 4), 0), reinterpret_tensor(primals_11, (1, 4), (1, 1), 0), reinterpret_tensor(primals_10, (1, 4), (1, 1), 0), reinterpret_tensor(buf9, (4, 4), (1, 4), 0), reinterpret_tensor(primals_8, (1, 4), (1, 1), 0), reinterpret_tensor(primals_7, (1, 4), (1, 1), 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), )


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, 1), (1, 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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = 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, primals_10, primals_11, primals_12, primals_13, primals_14])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F


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

    def __init__(self, in_features, out_features, dropout, alpha, concat=True):
        super(GraphAttentionLayer, self).__init__()
        self.dropout = dropout
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.concat = concat
        self.W = nn.Parameter(nn.init.xavier_uniform_(torch.FloatTensor(
            in_features, out_features).type(torch.FloatTensor if torch.cuda
            .is_available() else torch.FloatTensor), gain=np.sqrt(2.0)),
            requires_grad=True)
        self.a1 = nn.Parameter(nn.init.xavier_uniform_(torch.FloatTensor(
            out_features, 1).type(torch.FloatTensor if torch.cuda.
            is_available() else torch.FloatTensor), gain=np.sqrt(2.0)),
            requires_grad=True)
        self.a2 = nn.Parameter(nn.init.xavier_uniform_(torch.FloatTensor(
            out_features, 1).type(torch.FloatTensor if torch.cuda.
            is_available() else torch.FloatTensor), gain=np.sqrt(2.0)),
            requires_grad=True)
        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, input, adj):
        h = torch.mm(input, self.W)
        h.size()[0]
        f_1 = h @ self.a1
        f_2 = h @ self.a2
        e = self.leakyrelu(f_1 + f_2.transpose(0, 1))
        zero_vec = -9000000000000000.0 * torch.ones_like(e)
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, h)
        if self.concat:
            return F.sigmoid(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, nfeat, nhid, dropout, alpha, nheads):
        super(GAT, self).__init__()
        self.dropout = dropout
        self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout,
            alpha=alpha, concat=True) for _ in range(nheads)]
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)

    def forward(self, x, adj):
        x = F.dropout(x, self.dropout, training=self.training)
        x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
        x = F.dropout(x, self.dropout, training=self.training)
        return x


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


def get_init_inputs():
    return [[], {'nfeat': 4, 'nhid': 4, 'dropout': 0.5, 'alpha': 4,
        'nheads': 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
import numpy as np
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_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, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9,
    in_ptr10, in_ptr11, in_ptr12, out_ptr0, out_ptr1, out_ptr2, out_ptr3,
    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])
    tmp38 = tl.load(in_ptr4 + 4 * x0, xmask, eviction_policy='evict_last').to(
        tl.int1)
    tmp39 = tl.load(in_ptr5 + x0, xmask)
    tmp40 = tl.load(in_ptr6 + 0)
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK])
    tmp46 = tl.load(in_ptr4 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp47 = tl.load(in_ptr6 + 1)
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK])
    tmp54 = tl.load(in_ptr4 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp55 = tl.load(in_ptr6 + 2)
    tmp56 = tl.broadcast_to(tmp55, [XBLOCK])
    tmp62 = tl.load(in_ptr4 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp63 = tl.load(in_ptr6 + 3)
    tmp64 = tl.broadcast_to(tmp63, [XBLOCK])
    tmp70 = tl.load(in_ptr7 + 4 * x0, xmask, eviction_policy='evict_last').to(
        tl.int1)
    tmp71 = tl.load(in_ptr8 + x0, xmask)
    tmp72 = tl.load(in_ptr9 + 0)
    tmp73 = tl.broadcast_to(tmp72, [XBLOCK])
    tmp78 = tl.load(in_ptr7 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp79 = tl.load(in_ptr9 + 1)
    tmp80 = tl.broadcast_to(tmp79, [XBLOCK])
    tmp86 = tl.load(in_ptr7 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp87 = tl.load(in_ptr9 + 2)
    tmp88 = tl.broadcast_to(tmp87, [XBLOCK])
    tmp94 = tl.load(in_ptr7 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp95 = tl.load(in_ptr9 + 3)
    tmp96 = tl.broadcast_to(tmp95, [XBLOCK])
    tmp102 = tl.load(in_ptr10 + 4 * x0, xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp103 = tl.load(in_ptr11 + x0, xmask)
    tmp104 = tl.load(in_ptr12 + 0)
    tmp105 = tl.broadcast_to(tmp104, [XBLOCK])
    tmp110 = tl.load(in_ptr10 + (1 + 4 * x0), xmask, eviction_policy=
        'evict_last').to(tl.int1)
    tmp111 = tl.load(in_ptr12 + 1)
    tmp112 = tl.broadcast_to(tmp111, [XBLOCK])
    tmp118 = tl.load(in_ptr10 + (2 + 4 * x0), xmask, eviction_policy=
        'evict_last').to(tl.int1)
    tmp119 = tl.load(in_ptr12 + 2)
    tmp120 = tl.broadcast_to(tmp119, [XBLOCK])
    tmp126 = tl.load(in_ptr10 + (3 + 4 * x0), xmask, eviction_policy=
        'evict_last').to(tl.int1)
    tmp127 = tl.load(in_ptr12 + 3)
    tmp128 = tl.broadcast_to(tmp127, [XBLOCK])
    tmp5 = tmp2 + tmp4
    tmp6 = 4.0
    tmp7 = tmp5 * tmp6
    tmp8 = tl.where(tmp1, tmp5, tmp7)
    tmp9 = -8999999815811072.0
    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)
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42 * tmp6
    tmp44 = tl.where(tmp38, tmp42, tmp43)
    tmp45 = tl.where(tmp0, tmp44, tmp9)
    tmp49 = tmp39 + tmp48
    tmp50 = tmp49 * tmp6
    tmp51 = tl.where(tmp46, tmp49, tmp50)
    tmp52 = tl.where(tmp11, tmp51, tmp9)
    tmp53 = triton_helpers.maximum(tmp45, tmp52)
    tmp57 = tmp39 + tmp56
    tmp58 = tmp57 * tmp6
    tmp59 = tl.where(tmp54, tmp57, tmp58)
    tmp60 = tl.where(tmp20, tmp59, tmp9)
    tmp61 = triton_helpers.maximum(tmp53, tmp60)
    tmp65 = tmp39 + tmp64
    tmp66 = tmp65 * tmp6
    tmp67 = tl.where(tmp62, tmp65, tmp66)
    tmp68 = tl.where(tmp29, tmp67, tmp9)
    tmp69 = triton_helpers.maximum(tmp61, tmp68)
    tmp74 = tmp71 + tmp73
    tmp75 = tmp74 * tmp6
    tmp76 = tl.where(tmp70, tmp74, tmp75)
    tmp77 = tl.where(tmp0, tmp76, tmp9)
    tmp81 = tmp71 + tmp80
    tmp82 = tmp81 * tmp6
    tmp83 = tl.where(tmp78, tmp81, tmp82)
    tmp84 = tl.where(tmp11, tmp83, tmp9)
    tmp85 = triton_helpers.maximum(tmp77, tmp84)
    tmp89 = tmp71 + tmp88
    tmp90 = tmp89 * tmp6
    tmp91 = tl.where(tmp86, tmp89, tmp90)
    tmp92 = tl.where(tmp20, tmp91, tmp9)
    tmp93 = triton_helpers.maximum(tmp85, tmp92)
    tmp97 = tmp71 + tmp96
    tmp98 = tmp97 * tmp6
    tmp99 = tl.where(tmp94, tmp97, tmp98)
    tmp100 = tl.where(tmp29, tmp99, tmp9)
    tmp101 = triton_helpers.maximum(tmp93, tmp100)
    tmp106 = tmp103 + tmp105
    tmp107 = tmp106 * tmp6
    tmp108 = tl.where(tmp102, tmp106, tmp107)
    tmp109 = tl.where(tmp0, tmp108, tmp9)
    tmp113 = tmp103 + tmp112
    tmp114 = tmp113 * tmp6
    tmp115 = tl.where(tmp110, tmp113, tmp114)
    tmp116 = tl.where(tmp11, tmp115, tmp9)
    tmp117 = triton_helpers.maximum(tmp109, tmp116)
    tmp121 = tmp103 + tmp120
    tmp122 = tmp121 * tmp6
    tmp123 = tl.where(tmp118, tmp121, tmp122)
    tmp124 = tl.where(tmp20, tmp123, tmp9)
    tmp125 = triton_helpers.maximum(tmp117, tmp124)
    tmp129 = tmp103 + tmp128
    tmp130 = tmp129 * tmp6
    tmp131 = tl.where(tmp126, tmp129, tmp130)
    tmp132 = tl.where(tmp29, tmp131, tmp9)
    tmp133 = triton_helpers.maximum(tmp125, tmp132)
    tl.store(out_ptr0 + x0, tmp37, xmask)
    tl.store(out_ptr1 + x0, tmp69, xmask)
    tl.store(out_ptr2 + x0, tmp101, xmask)
    tl.store(out_ptr3 + x0, tmp133, xmask)


@triton.jit
def triton_poi_fused__softmax_add_leaky_relu_mul_where_3(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9,
    in_ptr10, in_ptr11, in_ptr12, in_ptr13, in_ptr14, in_ptr15, in_ptr16,
    out_ptr0, out_ptr1, out_ptr2, out_ptr3, 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')
    tmp13 = tl.load(in_ptr5 + x2, xmask).to(tl.int1)
    tmp14 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr7 + x0, xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr8 + x1, xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr9 + x2, xmask).to(tl.int1)
    tmp24 = tl.load(in_ptr10 + x1, xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr11 + x0, xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr12 + x1, xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr13 + x2, xmask).to(tl.int1)
    tmp34 = tl.load(in_ptr14 + x1, xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr15 + x0, xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr16 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp5 = 4.0
    tmp6 = tmp4 * tmp5
    tmp7 = tl.where(tmp1, tmp4, tmp6)
    tmp8 = -8999999815811072.0
    tmp9 = tl.where(tmp0, tmp7, tmp8)
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 * tmp5
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tl.where(tmp0, tmp18, tmp8)
    tmp21 = tmp19 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 * tmp5
    tmp28 = tl.where(tmp23, tmp26, tmp27)
    tmp29 = tl.where(tmp0, tmp28, tmp8)
    tmp31 = tmp29 - tmp30
    tmp32 = tl_math.exp(tmp31)
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 * tmp5
    tmp38 = tl.where(tmp33, tmp36, tmp37)
    tmp39 = tl.where(tmp0, tmp38, tmp8)
    tmp41 = tmp39 - tmp40
    tmp42 = tl_math.exp(tmp41)
    tl.store(out_ptr0 + x2, tmp12, xmask)
    tl.store(out_ptr1 + x2, tmp22, xmask)
    tl.store(out_ptr2 + x2, tmp32, xmask)
    tl.store(out_ptr3 + x2, tmp42, 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)


@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp4, tmp6, tmp7)
    tmp9 = tmp0 >= tmp3
    tmp10 = tl.full([1], 8, tl.int64)
    tmp11 = tmp0 < tmp10
    tmp12 = tmp9 & tmp11
    tmp13 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp12 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp12, tmp14, tmp15)
    tmp17 = tmp0 >= tmp10
    tmp18 = tl.full([1], 12, tl.int64)
    tmp19 = tmp0 < tmp18
    tmp20 = tmp17 & tmp19
    tmp21 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp22 = tl.sigmoid(tmp21)
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp25 = tmp0 >= tmp18
    tl.full([1], 16, tl.int64)
    tmp28 = tl.load(in_ptr3 + (4 * x1 + (-12 + x0)), tmp25 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp29 = tl.sigmoid(tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp25, tmp29, tmp30)
    tmp32 = tl.where(tmp20, tmp24, tmp31)
    tmp33 = tl.where(tmp12, tmp16, tmp32)
    tmp34 = tl.where(tmp4, tmp8, tmp33)
    tl.store(out_ptr0 + x2, tmp34, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 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, 1), (1, 1))
    assert_size_stride(primals_8, (4, 1), (1, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4, 1), (1, 1))
    assert_size_stride(primals_11, (4, 1), (1, 1))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4, 1), (1, 1))
    assert_size_stride(primals_14, (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_1, primals_2, out=buf0)
        del primals_2
        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
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_6, out=buf9)
        del primals_6
        buf10 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf9, primals_7, out=buf10)
        buf11 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf9, primals_8, out=buf11)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_add_leaky_relu_0[grid(16)](buf10, buf11, buf12, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf17 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_9, out=buf17)
        del primals_9
        buf18 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf17, primals_10, out=buf18)
        buf19 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf17, primals_11, out=buf19)
        buf20 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_add_leaky_relu_0[grid(16)](buf18, buf19, buf20, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf25 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_12, out=buf25)
        del primals_12
        buf26 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf25, primals_13, out=buf26)
        buf27 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf25, primals_14, out=buf27)
        buf28 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_add_leaky_relu_0[grid(16)](buf26, buf27, buf28, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf21 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf29 = 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, buf12, buf10, buf11, buf20, buf18, buf19,
            buf28, buf26, buf27, buf5, buf13, buf21, buf29, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf30 = 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, buf12, buf10, buf11, buf13, buf20,
            buf18, buf19, buf21, buf28, buf26, buf27, buf29, buf6, buf14,
            buf22, buf30, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf1
        del buf10
        del buf11
        del buf13
        del buf18
        del buf19
        del buf2
        del buf21
        del buf26
        del buf27
        del buf29
        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.mm(buf7, buf0, out=buf8)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(16)](buf14, buf15, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf16 = buf14
        del buf14
        extern_kernels.mm(buf15, buf9, out=buf16)
        buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(16)](buf22, buf23, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf24 = buf22
        del buf22
        extern_kernels.mm(buf23, buf17, out=buf24)
        buf31 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(16)](buf30, buf31, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf32 = buf30
        del buf30
        extern_kernels.mm(buf31, buf25, out=buf32)
        buf33 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_poi_fused_cat_5[grid(64)](buf8, buf16, buf24, buf32, buf33, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
    return (buf33, buf3, buf4, buf7, buf8, buf12, buf15, buf16, buf20,
        buf23, buf24, buf28, buf31, buf32, reinterpret_tensor(buf25, (4, 4),
        (1, 4), 0), reinterpret_tensor(primals_14, (1, 4), (1, 1), 0),
        reinterpret_tensor(primals_13, (1, 4), (1, 1), 0),
        reinterpret_tensor(primals_1, (4, 4), (1, 4), 0),
        reinterpret_tensor(buf17, (4, 4), (1, 4), 0), reinterpret_tensor(
        primals_11, (1, 4), (1, 1), 0), reinterpret_tensor(primals_10, (1, 
        4), (1, 1), 0), reinterpret_tensor(buf9, (4, 4), (1, 4), 0),
        reinterpret_tensor(primals_8, (1, 4), (1, 1), 0),
        reinterpret_tensor(primals_7, (1, 4), (1, 1), 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))


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

    def __init__(self, in_features, out_features, dropout, alpha, concat=True):
        super(GraphAttentionLayer, self).__init__()
        self.dropout = dropout
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.concat = concat
        self.W = nn.Parameter(nn.init.xavier_uniform_(torch.FloatTensor(
            in_features, out_features).type(torch.FloatTensor if torch.cuda
            .is_available() else torch.FloatTensor), gain=np.sqrt(2.0)),
            requires_grad=True)
        self.a1 = nn.Parameter(nn.init.xavier_uniform_(torch.FloatTensor(
            out_features, 1).type(torch.FloatTensor if torch.cuda.
            is_available() else torch.FloatTensor), gain=np.sqrt(2.0)),
            requires_grad=True)
        self.a2 = nn.Parameter(nn.init.xavier_uniform_(torch.FloatTensor(
            out_features, 1).type(torch.FloatTensor if torch.cuda.
            is_available() else torch.FloatTensor), gain=np.sqrt(2.0)),
            requires_grad=True)
        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, input, adj):
        h = torch.mm(input, self.W)
        h.size()[0]
        f_1 = h @ self.a1
        f_2 = h @ self.a2
        e = self.leakyrelu(f_1 + f_2.transpose(0, 1))
        zero_vec = -9000000000000000.0 * torch.ones_like(e)
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, h)
        if self.concat:
            return F.sigmoid(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, nfeat, nhid, dropout, alpha, nheads):
        super(GATNew, self).__init__()
        self.dropout = dropout
        self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout,
            alpha=alpha, concat=True) for _ in range(nheads)]
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)

    def forward(self, input_0, input_1):
        primals_1 = self.attention_0.W
        primals_3 = self.attention_0.a1
        primals_4 = self.attention_0.a2
        primals_2 = self.attention_1.W
        primals_7 = self.attention_1.a1
        primals_8 = self.attention_1.a2
        primals_5 = self.attention_2.W
        primals_10 = self.attention_2.a1
        primals_11 = self.attention_2.a2
        primals_6 = self.attention_3.W
        primals_13 = self.attention_3.a1
        primals_14 = self.attention_3.a2
        primals_9 = input_0
        primals_12 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0]
