# 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_2/inductor_cache/64/c647ba4473m7bdodonwf4zd5fpqykkjc22jacgwr6a5w2ebmvh6o.py
# Topologically Sorted Source Nodes: [logits, e], Original ATen: [aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   e => gt
#   logits => add
# 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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_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_2/inductor_cache/4u/c4ugbu6edk7upykkevfboxwsoz5gilzy6tbgcpgu2nqxrwbgdqii.py
# Topologically Sorted Source Nodes: [gt], Original ATen: [aten.gt]
# Source node to ATen node mapping:
#   gt => gt_1
# Graph fragment:
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%primals_5, 0), kwargs = {})
triton_poi_fused_gt_1 = async_compile.triton('triton_poi_fused_gt_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gt_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_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_2/inductor_cache/k6/ck6yo7e2kf3yevq342mbokylwltuaflzr37nmzjalq56yz3qhjzn.py
# Topologically Sorted Source Nodes: [mean_h], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   mean_h => mean
# Graph fragment:
#   %mean : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%mm, [0], True), kwargs = {})
triton_poi_fused_mean_2 = async_compile.triton('triton_poi_fused_mean_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: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mean_2(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 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/fi/cfine3a5lyy5u5dy5dib5vv3kjt7uwmoovjltml5itfrwauzaygi.py
# Topologically Sorted Source Nodes: [h_all], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   h_all => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%mm, %mean],), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    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 + (x0 + (4*x1)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/dy/cdyczyvxkwwu6tbzlpyrujoulq53lrq3qlberss42efb66qah67b.py
# Topologically Sorted Source Nodes: [glob_logit, e_diffused], Original ATen: [aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   e_diffused => gt_2
#   glob_logit => add_1
# Graph fragment:
#   %add_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_1, %mm_3), kwargs = {})
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_1, 0), kwargs = {})
triton_poi_fused_add_leaky_relu_4 = async_compile.triton('triton_poi_fused_add_leaky_relu_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_leaky_relu_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_leaky_relu_4(in_ptr0, in_ptr1, 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 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = 0.0
    tmp5 = tmp3 > tmp4
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/hb/chbitbaat24kkfrl27cbxmorhuea3kagv4x7ibqucsn3jbhfpgcb.py
# Topologically Sorted Source Nodes: [e_all], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   e_all => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_1, %where_2], -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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i1', 1: '*i1', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = (xindex // 5)
    x2 = xindex
    tmp22 = tl.load(in_ptr5 + (0))
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    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).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0).to(tl.int1)
    tmp7 = tl.load(in_ptr2 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tl.load(in_ptr3 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = 4.0
    tmp11 = tmp9 * tmp10
    tmp12 = tl.where(tmp6, tmp9, tmp11)
    tmp13 = -8999999815811072.0
    tmp14 = tl.where(tmp5, tmp12, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp4, tmp14, tmp15)
    tmp17 = tmp0 >= tmp3
    tmp18 = tl.full([1], 5, tl.int64)
    tmp19 = tmp0 < tmp18
    tmp20 = tl.load(in_ptr4 + (x1), tmp17 & xmask, eviction_policy='evict_last', other=0.0).to(tl.int1)
    tmp21 = tl.load(in_ptr2 + (x1), tmp17 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp24 * tmp10
    tmp26 = tl.where(tmp20, tmp24, tmp25)
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp17, tmp26, tmp27)
    tmp29 = tl.where(tmp4, tmp16, tmp28)
    tl.store(out_ptr0 + (x2), tmp29, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/4y/c4yvcbzfkwe2gfxwg7euliky4odcgeiuzl4yiy6e5giyzsyffzpz.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention => amax, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%cat_1, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cat_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_6 = async_compile.triton('triton_poi_fused__softmax_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_6(in_ptr0, out_ptr0, out_ptr1, 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 + (5*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp1 - tmp8
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp8
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/3u/c3u255tgmt2u7jp7yh45taagbpgmbhwvgz4dokhlsolagppj62oo.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%cat_1, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cat_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %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_7 = async_compile.triton('triton_poi_fused__softmax_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_7(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 5)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/ii/ciirqyhqzonjd6mmxnrcmh7nz3ugitutkzxafzeco4wcbdlilc3t.py
# Topologically Sorted Source Nodes: [elu], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   elu => expm1, gt_3, mul_3, mul_5, where_3
# Graph fragment:
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mm_4, 0), kwargs = {})
#   %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_4, 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_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %mul_3, %mul_5), kwargs = {})
triton_poi_fused_elu_8 = async_compile.triton('triton_poi_fused_elu_8', '''
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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_elu_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_8(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')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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))
    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: [logit_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: [logit_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: [logits, 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
        buf5 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean_h], Original ATen: [aten.mean]
        triton_poi_fused_mean_2.run(buf0, buf5, 4, grid=grid(4), stream=stream0)
        buf6 = empty_strided_cuda((5, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_all], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf0, buf5, buf6, 20, grid=grid(20), stream=stream0)
        buf7 = empty_strided_cuda((1, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [glob_logit_2], Original ATen: [aten.mm]
        extern_kernels.mm(buf5, primals_4, out=buf7)
        buf8 = empty_strided_cuda((4, 1), (1, 1), torch.bool)
        # Topologically Sorted Source Nodes: [glob_logit, e_diffused], Original ATen: [aten.add, aten.leaky_relu]
        triton_poi_fused_add_leaky_relu_4.run(buf1, buf7, buf8, 4, grid=grid(4), stream=stream0)
        buf9 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [e_all], Original ATen: [aten.cat]
        triton_poi_fused_cat_5.run(buf4, buf3, buf1, buf2, buf8, buf7, buf9, 20, grid=grid(20), stream=stream0)
        del buf7
        buf10 = reinterpret_tensor(buf2, (4, 1), (1, 4), 0); del buf2  # reuse
        buf11 = reinterpret_tensor(buf1, (4, 1), (1, 4), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_6.run(buf9, buf10, buf11, 4, grid=grid(4), stream=stream0)
        buf12 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_7.run(buf12, buf10, buf11, 20, grid=grid(20), stream=stream0)
        del buf10
        del buf11
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_out], Original ATen: [aten.mm]
        extern_kernels.mm(buf12, buf6, out=buf13)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [elu], Original ATen: [aten.elu]
        triton_poi_fused_elu_8.run(buf13, buf14, 16, grid=grid(16), stream=stream0)
    return (buf14, buf3, buf4, buf8, buf12, buf13, reinterpret_tensor(buf6, (4, 5), (1, 4), 0), reinterpret_tensor(buf5, (4, 1), (1, 4), 0), reinterpret_tensor(primals_4, (1, 4), (1, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 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)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
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
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_mean_2(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 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 5, tl.int64)
    tmp9 = tl.load(in_ptr1 + x0, tmp6 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_leaky_relu_4(in_ptr0, in_ptr1, 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 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = 0.0
    tmp5 = tmp3 > tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp22 = tl.load(in_ptr5 + 0)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    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).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0).to(tl.int1)
    tmp7 = tl.load(in_ptr2 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp8 = tl.load(in_ptr3 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = 4.0
    tmp11 = tmp9 * tmp10
    tmp12 = tl.where(tmp6, tmp9, tmp11)
    tmp13 = -8999999815811072.0
    tmp14 = tl.where(tmp5, tmp12, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp4, tmp14, tmp15)
    tmp17 = tmp0 >= tmp3
    tl.full([1], 5, tl.int64)
    tmp20 = tl.load(in_ptr4 + x1, tmp17 & xmask, eviction_policy=
        'evict_last', other=0.0).to(tl.int1)
    tmp21 = tl.load(in_ptr2 + x1, tmp17 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp24 * tmp10
    tmp26 = tl.where(tmp20, tmp24, tmp25)
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp17, tmp26, tmp27)
    tmp29 = tl.where(tmp4, tmp16, tmp28)
    tl.store(out_ptr0 + x2, tmp29, xmask)


@triton.jit
def triton_poi_fused__softmax_6(in_ptr0, out_ptr0, out_ptr1, 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 + 5 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp1 - tmp8
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp8
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp22, xmask)


@triton.jit
def triton_poi_fused__softmax_7(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 5
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_elu_8(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)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf0, primals_3, out=buf1)
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf0, primals_4, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_leaky_relu_0[grid(16)](buf1, buf2, buf3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_gt_1[grid(16)](primals_5, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        triton_poi_fused_mean_2[grid(4)](buf0, buf5, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        buf6 = empty_strided_cuda((5, 4), (4, 1), torch.float32)
        triton_poi_fused_cat_3[grid(20)](buf0, buf5, buf6, 20, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((1, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf5, primals_4, out=buf7)
        buf8 = empty_strided_cuda((4, 1), (1, 1), torch.bool)
        triton_poi_fused_add_leaky_relu_4[grid(4)](buf1, buf7, buf8, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        triton_poi_fused_cat_5[grid(20)](buf4, buf3, buf1, buf2, buf8, buf7,
            buf9, 20, XBLOCK=32, num_warps=1, num_stages=1)
        del buf7
        buf10 = reinterpret_tensor(buf2, (4, 1), (1, 4), 0)
        del buf2
        buf11 = reinterpret_tensor(buf1, (4, 1), (1, 4), 0)
        del buf1
        triton_poi_fused__softmax_6[grid(4)](buf9, buf10, buf11, 4, XBLOCK=
            4, num_warps=1, num_stages=1)
        buf12 = buf9
        del buf9
        triton_poi_fused__softmax_7[grid(20)](buf12, buf10, buf11, 20,
            XBLOCK=32, num_warps=1, num_stages=1)
        del buf10
        del buf11
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf12, buf6, out=buf13)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_elu_8[grid(16)](buf13, buf14, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return buf14, buf3, buf4, buf8, buf12, buf13, reinterpret_tensor(buf6,
        (4, 5), (1, 4), 0), reinterpret_tensor(buf5, (4, 1), (1, 4), 0
        ), reinterpret_tensor(primals_4, (1, 4), (1, 1), 0
        ), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), reinterpret_tensor(
        primals_3, (1, 4), (1, 1), 0), reinterpret_tensor(primals_2, (4, 4),
        (1, 4), 0)


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

    def __init__(self, in_features, out_features, dropout, alpha):
        super(GraphDiffusedAttentionLayerNew, self).__init__()
        self.dropout = dropout
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features),
            dtype=torch.float))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.a_1 = nn.Parameter(torch.zeros(size=(out_features, 1), dtype=
            torch.float))
        nn.init.xavier_uniform_(self.a_1.data, gain=1.414)
        self.a_2 = nn.Parameter(torch.zeros(size=(out_features, 1), dtype=
            torch.float))
        nn.init.xavier_uniform_(self.a_2.data, gain=1.414)
        self.leakyrelu = nn.LeakyReLU(self.alpha)

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

    def forward(self, input_0, input_1):
        primals_1 = self.W
        primals_3 = self.a_1
        primals_4 = self.a_2
        primals_2 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
