# 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_1/inductor_cache/ep/cephjjbdwu6testt2i7mcyzbq3s4pfvk2ixd4n4wnesozrntcmpj.py
# Topologically Sorted Source Nodes: [hidden_feat], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   hidden_feat => tanh
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {})
triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_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: '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_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], '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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/4h/c4hjo2rydfss6y37bfpikr6qruu37ownn7q4wldxnigmo3nyy2dp.py
# Topologically Sorted Source Nodes: [attn_weight], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_weight => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %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=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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__softmax_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__softmax_1(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 = tmp0 - tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp2 / tmp2
    tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/hj/chjwuldmh3s4mh4d5y6hzziociipznxarw27tdjn3vi3p4wi4peb.py
# Topologically Sorted Source Nodes: [feat_2, hidden_feat_2], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   feat_2 => add
#   hidden_feat_2 => tanh_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_8, %expand), kwargs = {})
#   %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_tanh_2 = async_compile.triton('triton_poi_fused_add_tanh_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_2', 'mutated_arg_names': ['in_out_ptr0'], '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_add_tanh_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/us/cusyqra2bg2vbuuxs26cz3sihdy7z4jtgux6v3orbezw7iu5eot5.py
# Topologically Sorted Source Nodes: [attn_weight_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_weight_1 => amax_1, exp_1, sub_1
# Graph fragment:
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_11, [1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_11, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=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_3', '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_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    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_1/inductor_cache/wg/cwgoxrbhmwbtk44l4btvoekxvdbagjk6vkr5n42rshizrndijbnh.py
# Topologically Sorted Source Nodes: [attn_weight_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_weight_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=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), 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=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_4', '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_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_1/inductor_cache/2m/c2mv4hqncezx6odxrxznasmrym4v2lk6oeijrlf422aetqukoofq.py
# Topologically Sorted Source Nodes: [feat_4, hidden_feat_4], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   feat_4 => add_1
#   hidden_feat_4 => tanh_2
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_16, %expand_1), kwargs = {})
#   %tanh_2 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_tanh_5 = async_compile.triton('triton_poi_fused_add_tanh_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: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_5', 'mutated_arg_names': ['in_out_ptr0'], '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_add_tanh_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + (x2), 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, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18 = 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, ))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (1, 4), (4, 1))
    assert_size_stride(primals_12, (1, ), (1, ))
    assert_size_stride(primals_13, (4, 4), (4, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    assert_size_stride(primals_15, (4, 4), (4, 1))
    assert_size_stride(primals_16, (4, ), (1, ))
    assert_size_stride(primals_17, (1, 4), (4, 1))
    assert_size_stride(primals_18, (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: [], Original ATen: []
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [hidden_feat], Original ATen: [aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_tanh_0.run(buf1, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weight], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf3, buf4, 4, grid=grid(4), stream=stream0)
        buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weight, view, attn_X], Original ATen: [aten._softmax, aten.view, aten.bmm]
        extern_kernels.bmm(buf4, reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf6)
        del primals_7
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf5, (4, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf7)
        buf8 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [feat_2, hidden_feat_2], Original ATen: [aten.add, aten.tanh]
        triton_poi_fused_add_tanh_2.run(buf8, primals_8, buf7, primals_10, 64, grid=grid(64), stream=stream0)
        del primals_10
        del primals_8
        buf10 = reinterpret_tensor(buf7, (16, 1), (1, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf10)
        del primals_12
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weight_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf10, buf11, 16, grid=grid(16), stream=stream0)
        buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weight_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.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: [attn_X_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf12, (4, 1, 4), (4, 0, 1), 0), primals_6, out=buf13)
        buf14 = reinterpret_tensor(buf12, (4, 4), (4, 1), 0); del buf12  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_13, (4, 4), (1, 4), 0), out=buf14)
        del primals_13
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf13, (4, 4), (4, 1), 0), reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf15)
        buf16 = reinterpret_tensor(buf14, (4, 1, 4), (4, 4, 1), 0); del buf14  # reuse
        # Topologically Sorted Source Nodes: [feat_4, hidden_feat_4], Original ATen: [aten.add, aten.tanh]
        triton_poi_fused_add_tanh_5.run(buf16, primals_14, buf15, primals_16, 16, grid=grid(16), stream=stream0)
        del primals_14
        del primals_16
        buf18 = reinterpret_tensor(buf4, (4, 1), (1, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [linear_7], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_18, reinterpret_tensor(buf16, (4, 4), (4, 1), 0), reinterpret_tensor(primals_17, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf18)
        del primals_18
        buf19 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weight_2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf18, buf19, 4, grid=grid(4), stream=stream0)
        buf20 = reinterpret_tensor(buf15, (4, 1, 4), (4, 4, 1), 0); del buf15  # reuse
        # Topologically Sorted Source Nodes: [attn_weight_2, view_8, attn_X_2], Original ATen: [aten._softmax, aten.view, aten.bmm]
        extern_kernels.bmm(buf19, reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 1), 0), out=buf20)
        del buf19
    return (reinterpret_tensor(buf20, (4, 4), (4, 1), 0), reinterpret_tensor(buf13, (4, 4), (4, 1), 0), primals_1, primals_6, buf1, buf3, reinterpret_tensor(buf5, (4, 4), (4, 1), 0), buf8, buf10, reinterpret_tensor(buf13, (4, 4), (4, 1), 0), buf16, buf18, primals_17, primals_15, primals_11, primals_9, primals_4, )


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, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), 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, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((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, primals_15, primals_16, primals_17, primals_18])
    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.functional as F
import torch.nn as nn
import torch.autograd
import torch.nn


class Attn(nn.Module):
    """
    Unit attention operation for alternating co-attention.
    ``https://arxiv.org/pdf/1606.00061.pdf``
    
    .. math::
        \\begin{array}{ll}
        H = \\tanh(W_x * X + (W_g * g)) \\\\
        a = softmax(w_{hx}^T * H}) \\\\
        output = sum a_i * x_i
        \\end{array}

    Args:
        num_hidden: Number of output hidden size
        input_feat_size: Feature size of input image
        guidance_size:  Feature size of attention guidance [default: 0]
        dropout: Dropout rate of attention operation [default: 0.5]

    Inputs:
        - **X** (batch, input_seq_size, input_feat_size): Input image feature
        - **g** (batch, guidance_size): Attention guidance
    """

    def __init__(self, num_hidden, input_feat_size, guidance_size=0,
        dropout=0.5):
        super(Attn, self).__init__()
        self.num_hidden = num_hidden
        self.input_feat_size = input_feat_size
        self.guidance_size = guidance_size
        self.dropout = nn.Dropout(dropout) if dropout > 0 else None
        self.W_x = nn.Linear(input_feat_size, num_hidden)
        if guidance_size > 0:
            self.W_g = nn.Linear(guidance_size, num_hidden)
        self.W_hx = nn.Linear(num_hidden, 1)

    def forward(self, X, g=None):
        _batch_size, input_seq_size, input_feat_size = X.size()
        feat = self.W_x(X)
        if g is not None:
            g_emb = self.W_g(g).view(-1, 1, self.num_hidden)
            feat = feat + g_emb.expand_as(feat)
        hidden_feat = torch.tanh(feat)
        if self.dropout is not None:
            hidden_feat = self.dropout(hidden_feat)
        attn_weight = F.softmax(self.W_hx(hidden_feat), dim=1)
        attn_X = torch.bmm(attn_weight.view(-1, 1, input_seq_size), X.view(
            -1, input_seq_size, input_feat_size))
        return attn_X.view(-1, input_feat_size)


class AlterCoAttn(nn.Module):
    """
    Alternatiing Co-Attention mechanism according to ``https://arxiv.org/pdf/1606.00061.pdf``

    .. math::
        \\begin{array}{ll}
            \\hat{s} = Attn(Q, 0) \\\\
            \\hat{v} = Attn(\\hat{s}, V) \\\\
            \\hat{q} = Attn(Q, \\hat{v})
        \\end{array}
    """

    def __init__(self, num_hidden, img_feat_size, ques_feat_size, dropout=0.5):
        super(AlterCoAttn, self).__init__()
        self.num_hidden = num_hidden
        self.img_feat_size = img_feat_size
        self.ques_feat_size = ques_feat_size
        self.attn1 = Attn(num_hidden, ques_feat_size, guidance_size=0,
            dropout=dropout)
        self.attn2 = Attn(num_hidden, img_feat_size, ques_feat_size,
            dropout=dropout)
        self.attn3 = Attn(num_hidden, ques_feat_size, img_feat_size,
            dropout=dropout)

    def forward(self, ques_feat, img_feat):
        """
        : param ques_feat: [batch, ques_feat_size]
        : param img_feat: [batch, img_seq_size, ques_feat_size]
        """
        ques_self_attn = self.attn1(ques_feat.unsqueeze(1), None)
        img_attn_feat = self.attn2(img_feat, ques_self_attn)
        ques_attn_feat = self.attn3(ques_feat.unsqueeze(1), img_attn_feat)
        return ques_attn_feat, img_attn_feat


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


def get_init_inputs():
    return [[], {'num_hidden': 4, 'img_feat_size': 4, 'ques_feat_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn.functional as F
import torch.nn as nn
import torch.autograd
import torch.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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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 = tmp0 - tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp2 / tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_tanh_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    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_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_add_tanh_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, 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, primals_15, primals_16, primals_17, primals_18
        ) = 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,))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (1, 4), (4, 1))
    assert_size_stride(primals_12, (1,), (1,))
    assert_size_stride(primals_13, (4, 4), (4, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4, 4), (4, 1))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (1, 4), (4, 1))
    assert_size_stride(primals_18, (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, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(16)](buf1, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha
            =1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        triton_poi_fused__softmax_1[grid(4)](buf3, buf4, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(buf4, reinterpret_tensor(primals_1, (4, 1, 4), (
            4, 4, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf6)
        del primals_7
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf7)
        buf8 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0)
        del buf6
        triton_poi_fused_add_tanh_2[grid(64)](buf8, primals_8, buf7,
            primals_10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_10
        del primals_8
        buf10 = reinterpret_tensor(buf7, (16, 1), (1, 1), 0)
        del buf7
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf8, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_11, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf10)
        del primals_12
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_3[grid(16)](buf10, buf11, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused__softmax_4[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, 0, 1), 
            0), primals_6, out=buf13)
        buf14 = reinterpret_tensor(buf12, (4, 4), (4, 1), 0)
        del buf12
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_13, (4, 4),
            (1, 4), 0), out=buf14)
        del primals_13
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf15)
        buf16 = reinterpret_tensor(buf14, (4, 1, 4), (4, 4, 1), 0)
        del buf14
        triton_poi_fused_add_tanh_5[grid(16)](buf16, primals_14, buf15,
            primals_16, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_14
        del primals_16
        buf18 = reinterpret_tensor(buf4, (4, 1), (1, 1), 0)
        del buf4
        extern_kernels.addmm(primals_18, reinterpret_tensor(buf16, (4, 4),
            (4, 1), 0), reinterpret_tensor(primals_17, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf18)
        del primals_18
        buf19 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        triton_poi_fused__softmax_1[grid(4)](buf18, buf19, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf20 = reinterpret_tensor(buf15, (4, 1, 4), (4, 4, 1), 0)
        del buf15
        extern_kernels.bmm(buf19, reinterpret_tensor(primals_1, (4, 1, 4),
            (4, 4, 1), 0), out=buf20)
        del buf19
    return reinterpret_tensor(buf20, (4, 4), (4, 1), 0), reinterpret_tensor(
        buf13, (4, 4), (4, 1), 0
        ), primals_1, primals_6, buf1, buf3, reinterpret_tensor(buf5, (4, 4
        ), (4, 1), 0), buf8, buf10, reinterpret_tensor(buf13, (4, 4), (4, 1), 0
        ), buf16, buf18, primals_17, primals_15, primals_11, primals_9, primals_4


class Attn(nn.Module):
    """
    Unit attention operation for alternating co-attention.
    ``https://arxiv.org/pdf/1606.00061.pdf``
    
    .. math::
        \\begin{array}{ll}
        H = \\tanh(W_x * X + (W_g * g)) \\\\
        a = softmax(w_{hx}^T * H}) \\\\
        output = sum a_i * x_i
        \\end{array}

    Args:
        num_hidden: Number of output hidden size
        input_feat_size: Feature size of input image
        guidance_size:  Feature size of attention guidance [default: 0]
        dropout: Dropout rate of attention operation [default: 0.5]

    Inputs:
        - **X** (batch, input_seq_size, input_feat_size): Input image feature
        - **g** (batch, guidance_size): Attention guidance
    """

    def __init__(self, num_hidden, input_feat_size, guidance_size=0,
        dropout=0.5):
        super(Attn, self).__init__()
        self.num_hidden = num_hidden
        self.input_feat_size = input_feat_size
        self.guidance_size = guidance_size
        self.dropout = nn.Dropout(dropout) if dropout > 0 else None
        self.W_x = nn.Linear(input_feat_size, num_hidden)
        if guidance_size > 0:
            self.W_g = nn.Linear(guidance_size, num_hidden)
        self.W_hx = nn.Linear(num_hidden, 1)

    def forward(self, X, g=None):
        _batch_size, input_seq_size, input_feat_size = X.size()
        feat = self.W_x(X)
        if g is not None:
            g_emb = self.W_g(g).view(-1, 1, self.num_hidden)
            feat = feat + g_emb.expand_as(feat)
        hidden_feat = torch.tanh(feat)
        if self.dropout is not None:
            hidden_feat = self.dropout(hidden_feat)
        attn_weight = F.softmax(self.W_hx(hidden_feat), dim=1)
        attn_X = torch.bmm(attn_weight.view(-1, 1, input_seq_size), X.view(
            -1, input_seq_size, input_feat_size))
        return attn_X.view(-1, input_feat_size)


class AlterCoAttnNew(nn.Module):
    """
    Alternatiing Co-Attention mechanism according to ``https://arxiv.org/pdf/1606.00061.pdf``

    .. math::
        \\begin{array}{ll}
            \\hat{s} = Attn(Q, 0) \\\\
            \\hat{v} = Attn(\\hat{s}, V) \\\\
            \\hat{q} = Attn(Q, \\hat{v})
        \\end{array}
    """

    def __init__(self, num_hidden, img_feat_size, ques_feat_size, dropout=0.5):
        super(AlterCoAttnNew, self).__init__()
        self.num_hidden = num_hidden
        self.img_feat_size = img_feat_size
        self.ques_feat_size = ques_feat_size
        self.attn1 = Attn(num_hidden, ques_feat_size, guidance_size=0,
            dropout=dropout)
        self.attn2 = Attn(num_hidden, img_feat_size, ques_feat_size,
            dropout=dropout)
        self.attn3 = Attn(num_hidden, ques_feat_size, img_feat_size,
            dropout=dropout)

    def forward(self, input_0, input_1):
        primals_1 = self.attn1.W_x.weight
        primals_3 = self.attn1.W_x.bias
        primals_4 = self.attn1.W_hx.weight
        primals_5 = self.attn1.W_hx.bias
        primals_2 = self.attn2.W_x.weight
        primals_8 = self.attn2.W_x.bias
        primals_7 = self.attn2.W_g.weight
        primals_10 = self.attn2.W_g.bias
        primals_11 = self.attn2.W_hx.weight
        primals_12 = self.attn2.W_hx.bias
        primals_9 = self.attn3.W_x.weight
        primals_14 = self.attn3.W_x.bias
        primals_13 = self.attn3.W_g.weight
        primals_16 = self.attn3.W_g.bias
        primals_17 = self.attn3.W_hx.weight
        primals_18 = self.attn3.W_hx.bias
        primals_15 = input_0
        primals_6 = 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,
            primals_15, primals_16, primals_17, primals_18])
        return output[0], output[1]
