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

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


# kernel path: runs/run_shard_6/inductor_cache/in/cinpsvuoyhz6qmlmbhyhbylx7r2qwlmioevovcpj3suugwg3n5qo.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_5), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ae/caezqhmlovmjdv4l3g6p6fwloiw7ux5sw3r3sazglrmyq4u6upnm.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone, aten.transpose]
# Source node to ATen node mapping:
#   contiguous => clone_2
# Graph fragment:
#   %clone_2 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
#   %permute_5 : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%clone_2, [0, 2, 1]), kwargs = {})
triton_poi_fused_clone_transpose_1 = async_compile.triton('triton_poi_fused_clone_transpose_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, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_transpose_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_clone_transpose_1(in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 4
    y3 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x1 + (4*y0)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y2 + (4*x1) + (16*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ga/cga5m2vg5zgpkcw67nk7tszo6ncbcvd6clrqkqmvkxiqxi4kajrv.py
# Topologically Sorted Source Nodes: [add, att, weight_one, max_1], Original ATen: [aten.add, aten._softmax, aten.max]
# Source node to ATen node mapping:
#   add => add
#   att => add_1
#   max_1 => max_1
#   weight_one => amax, exp, sub, sum_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %view_4), kwargs = {})
#   %add_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %bmm), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_1, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_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 = {})
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.dim](args = (%add_1, -1), kwargs = {})
triton_poi_fused__softmax_add_max_2 = async_compile.triton('triton_poi_fused__softmax_add_max_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i64', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_max_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_max_2(in_ptr0, in_ptr1, in_ptr2, 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)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (4*x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr2 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp0 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp0 + tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp0 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp31 = tmp4 > tmp8
    tmp32 = tmp4 == tmp8
    tmp33 = tmp4 != tmp4
    tmp34 = tmp8 != tmp8
    tmp35 = tmp33 > tmp34
    tmp36 = tmp31 | tmp35
    tmp37 = tmp33 & tmp34
    tmp38 = tmp32 | tmp37
    tmp39 = tl.full([1], 0, tl.int64)
    tmp40 = tl.full([1], 1, tl.int64)
    tmp41 = tmp39 < tmp40
    tmp42 = tmp38 & tmp41
    tmp43 = tmp36 | tmp42
    tmp44 = tl.where(tmp43, tmp4, tmp8)
    tmp45 = tl.where(tmp43, tmp39, tmp40)
    tmp46 = tmp44 > tmp13
    tmp47 = tmp44 == tmp13
    tmp48 = tmp44 != tmp44
    tmp49 = tmp13 != tmp13
    tmp50 = tmp48 > tmp49
    tmp51 = tmp46 | tmp50
    tmp52 = tmp48 & tmp49
    tmp53 = tmp47 | tmp52
    tmp54 = tl.full([1], 2, tl.int64)
    tmp55 = tmp45 < tmp54
    tmp56 = tmp53 & tmp55
    tmp57 = tmp51 | tmp56
    tmp58 = tl.where(tmp57, tmp44, tmp13)
    tmp59 = tl.where(tmp57, tmp45, tmp54)
    tmp60 = tmp58 > tmp18
    tmp61 = tmp58 == tmp18
    tmp62 = tmp58 != tmp58
    tmp63 = tmp18 != tmp18
    tmp64 = tmp62 > tmp63
    tmp65 = tmp60 | tmp64
    tmp66 = tmp62 & tmp63
    tmp67 = tmp61 | tmp66
    tmp68 = tl.full([1], 3, tl.int64)
    tmp69 = tmp59 < tmp68
    tmp70 = tmp67 & tmp69
    tmp71 = tmp65 | tmp70
    tmp72 = tl.where(tmp71, tmp58, tmp18)
    tmp73 = tl.where(tmp71, tmp59, tmp68)
    tl.store(out_ptr0 + (x2), tmp19, xmask)
    tl.store(out_ptr1 + (x2), tmp30, xmask)
    tl.store(out_ptr2 + (x2), tmp19, xmask)
    tl.store(out_ptr3 + (x2), tmp73, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/a3/ca3lcgz4fnidj4iucuuv4msywdjityy67vgy7haxvityad3nn65r.py
# Topologically Sorted Source Nodes: [add, att, weight_one], Original ATen: [aten.add, aten._softmax]
# Source node to ATen node mapping:
#   add => add
#   att => add_1
#   weight_one => div, exp, sub
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %view_4), kwargs = {})
#   %add_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %bmm), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_add_3 = async_compile.triton('triton_poi_fused__softmax_add_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=[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__softmax_add_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_add_3(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = (xindex // 4)
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp5 = tl.load(in_ptr2 + (x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(in_out_ptr0 + (x4), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/nl/cnla2cqa5fw6bip3brpt6zibngvgv3feeidyi5rvukf2am4vwmmr.py
# Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax_1 => amax_1, exp_1, sub_1
# Graph fragment:
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%getitem, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/qf/cqfbrg74qey6mqge6e7bnjziqndekcustllat4jl4solktk6wuem.py
# Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax_1 => div_1, sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), kwargs = {})
triton_poi_fused__softmax_5 = async_compile.triton('triton_poi_fused__softmax_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=[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_5', '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_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7g/c7gon7ymvitwxwni24kpsxc4pjwgwu6us3wzw6viazdk7ynicybf.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %bmm_1, %mul_1, %mul_2], -1), kwargs = {})
triton_poi_fused_cat_6 = async_compile.triton('triton_poi_fused_cat_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x3 = (xindex // 16)
    x2 = (xindex // 64)
    x4 = 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*x3) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x3) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + ((4*x3) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + ((4*x3) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 * tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tmp21 = tl.full([1], 16, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tl.load(in_ptr2 + ((4*x2) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr1 + ((4*x3) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + (x4), tmp30, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (1, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_dot], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 1), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_1, primals_5, buf2, 64, grid=grid(64), stream=stream0)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone, aten.transpose]
        triton_poi_fused_clone_transpose_1.run(primals_2, buf3, buf15, 16, 4, grid=grid(16, 4), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, contiguous, cross_dot], Original ATen: [aten.mul, aten.clone, aten.bmm]
        extern_kernels.bmm(buf2, buf3, out=buf4)
        del buf2
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [add, att, weight_one, max_1], Original ATen: [aten.add, aten._softmax, aten.max]
        triton_poi_fused__softmax_add_max_2.run(buf0, buf1, buf4, buf5, buf6, buf9, buf10, 16, grid=grid(16), stream=stream0)
        buf7 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [add, att, weight_one], Original ATen: [aten.add, aten._softmax]
        triton_poi_fused__softmax_add_3.run(buf7, buf0, buf1, buf5, buf6, 64, grid=grid(64), stream=stream0)
        del buf0
        del buf1
        del buf5
        buf8 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [output_one], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf7, primals_2, out=buf8)
        buf11 = reinterpret_tensor(buf6, (4, 4), (4, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf9, buf11, 16, grid=grid(16), stream=stream0)
        buf12 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_5.run(buf11, buf12, 16, grid=grid(16), stream=stream0)
        buf13 = reinterpret_tensor(buf11, (4, 1, 4), (4, 4, 1), 0); del buf11  # reuse
        # Topologically Sorted Source Nodes: [output_two], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf12, (4, 1, 4), (4, 4, 1), 0), primals_1, out=buf13)
        buf14 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_6.run(primals_1, buf8, buf13, buf14, 256, grid=grid(256), stream=stream0)
    return (buf14, primals_1, primals_2, buf7, buf8, buf12, buf13, reinterpret_tensor(buf10, (4, 4, 1), (4, 1, 1), 0), buf15, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((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
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F


class BiAttention(nn.Module):

    def __init__(self, input_size, dropout):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)
        self.input_linear = nn.Linear(input_size, 1, bias=False)
        self.memory_linear = nn.Linear(input_size, 1, bias=False)
        self.dot_scale = nn.Parameter(torch.Tensor(input_size).uniform_(1.0 /
            input_size ** 0.5))

    def forward(self, input, memory, mask=None):
        bsz, input_len, memory_len = input.size(0), input.size(1), memory.size(
            1)
        input = self.dropout(input)
        memory = self.dropout(memory)
        input_dot = self.input_linear(input)
        memory_dot = self.memory_linear(memory).view(bsz, 1, memory_len)
        cross_dot = torch.bmm(input * self.dot_scale, memory.permute(0, 2, 
            1).contiguous())
        att = input_dot + memory_dot + cross_dot
        if mask is not None:
            att = att - 1e+30 * (1 - mask[:, None])
        weight_one = F.softmax(att, dim=-1)
        output_one = torch.bmm(weight_one, memory)
        weight_two = F.softmax(att.max(dim=-1)[0], dim=-1).view(bsz, 1,
            input_len)
        output_two = torch.bmm(weight_two, input)
        return torch.cat([input, output_one, input * output_one, output_two *
            output_one], dim=-1)


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


def get_init_inputs():
    return [[], {'input_size': 4, 'dropout': 0.5}]

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 torch.nn as nn
import torch.utils.data
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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_transpose_1(in_ptr0, out_ptr0, out_ptr1, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 4
    y3 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x1 + 4 * y0), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 4 * x1 + 16 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_add_max_2(in_ptr0, in_ptr1, in_ptr2, 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
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr2 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr2 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp0 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp0 + tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp0 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp31 = tmp4 > tmp8
    tmp32 = tmp4 == tmp8
    tmp33 = tmp4 != tmp4
    tmp34 = tmp8 != tmp8
    tmp35 = tmp33 > tmp34
    tmp36 = tmp31 | tmp35
    tmp37 = tmp33 & tmp34
    tmp38 = tmp32 | tmp37
    tmp39 = tl.full([1], 0, tl.int64)
    tmp40 = tl.full([1], 1, tl.int64)
    tmp41 = tmp39 < tmp40
    tmp42 = tmp38 & tmp41
    tmp43 = tmp36 | tmp42
    tmp44 = tl.where(tmp43, tmp4, tmp8)
    tmp45 = tl.where(tmp43, tmp39, tmp40)
    tmp46 = tmp44 > tmp13
    tmp47 = tmp44 == tmp13
    tmp48 = tmp44 != tmp44
    tmp49 = tmp13 != tmp13
    tmp50 = tmp48 > tmp49
    tmp51 = tmp46 | tmp50
    tmp52 = tmp48 & tmp49
    tmp53 = tmp47 | tmp52
    tmp54 = tl.full([1], 2, tl.int64)
    tmp55 = tmp45 < tmp54
    tmp56 = tmp53 & tmp55
    tmp57 = tmp51 | tmp56
    tmp58 = tl.where(tmp57, tmp44, tmp13)
    tmp59 = tl.where(tmp57, tmp45, tmp54)
    tmp60 = tmp58 > tmp18
    tmp61 = tmp58 == tmp18
    tmp62 = tmp58 != tmp58
    tmp63 = tmp18 != tmp18
    tmp64 = tmp62 > tmp63
    tmp65 = tmp60 | tmp64
    tmp66 = tmp62 & tmp63
    tmp67 = tmp61 | tmp66
    tmp68 = tl.full([1], 3, tl.int64)
    tmp69 = tmp59 < tmp68
    tmp70 = tmp67 & tmp69
    tmp71 = tmp65 | tmp70
    tl.where(tmp71, tmp58, tmp18)
    tmp73 = tl.where(tmp71, tmp59, tmp68)
    tl.store(out_ptr0 + x2, tmp19, xmask)
    tl.store(out_ptr1 + x2, tmp30, xmask)
    tl.store(out_ptr2 + x2, tmp19, xmask)
    tl.store(out_ptr3 + x2, tmp73, xmask)


@triton.jit
def triton_poi_fused__softmax_add_3(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_out_ptr0 + x4, xmask)
    tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(in_out_ptr0 + x4, tmp9, 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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_5(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_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x3 = xindex // 16
    x2 = xindex // 64
    x4 = 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 * x3 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x3 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (4 * x3 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + (4 * x3 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 * tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp23 = tl.load(in_ptr2 + (4 * x2 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr1 + (4 * x3 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + x4, tmp30, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (1, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 1), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(64)](primals_1, primals_5, buf2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        triton_poi_fused_clone_transpose_1[grid(16, 4)](primals_2, buf3,
            buf15, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf2, buf3, out=buf4)
        del buf2
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.int64)
        triton_poi_fused__softmax_add_max_2[grid(16)](buf0, buf1, buf4,
            buf5, buf6, buf9, buf10, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf7 = buf4
        del buf4
        triton_poi_fused__softmax_add_3[grid(64)](buf7, buf0, buf1, buf5,
            buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf5
        buf8 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        extern_kernels.bmm(buf7, primals_2, out=buf8)
        buf11 = reinterpret_tensor(buf6, (4, 4), (4, 1), 0)
        del buf6
        triton_poi_fused__softmax_4[grid(16)](buf9, buf11, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf12 = buf9
        del buf9
        triton_poi_fused__softmax_5[grid(16)](buf11, buf12, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf13 = reinterpret_tensor(buf11, (4, 1, 4), (4, 4, 1), 0)
        del buf11
        extern_kernels.bmm(reinterpret_tensor(buf12, (4, 1, 4), (4, 4, 1), 
            0), primals_1, out=buf13)
        buf14 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_cat_6[grid(256)](primals_1, buf8, buf13, buf14, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
    return (buf14, primals_1, primals_2, buf7, buf8, buf12, buf13,
        reinterpret_tensor(buf10, (4, 4, 1), (4, 1, 1), 0), buf15)


class BiAttentionNew(nn.Module):

    def __init__(self, input_size, dropout):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)
        self.input_linear = nn.Linear(input_size, 1, bias=False)
        self.memory_linear = nn.Linear(input_size, 1, bias=False)
        self.dot_scale = nn.Parameter(torch.Tensor(input_size).uniform_(1.0 /
            input_size ** 0.5))

    def forward(self, input_0, input_1):
        primals_5 = self.dot_scale
        primals_3 = self.input_linear.weight
        primals_4 = self.memory_linear.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
