# 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_4/inductor_cache/n5/cn56qdig5m7ntgstzcc6mmhynryl3qbrs73pdgzlaburlen3vg3e.py
# Topologically Sorted Source Nodes: [H], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   H => 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=[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_tanh_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_tanh_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
    tmp3 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cy/ccyrgmfkbichexac5tjes27275w4oktywkikefvtkqa7hojoe3xa.py
# Topologically Sorted Source Nodes: [a, a_1, a_2, a_3], Original ATen: [aten._softmax, aten.mul, aten.sum, aten.squeeze]
# Source node to ATen node mapping:
#   a => amax, div, exp, sub, sum_1
#   a_1 => mul
#   a_2 => sum_2
#   a_3 => squeeze
# 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 = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, %div), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %squeeze : [num_users=2] = call_function[target=torch.ops.aten.squeeze.default](args = (%sum_2,), kwargs = {})
triton_poi_fused__softmax_mul_squeeze_sum_1 = async_compile.triton('triton_poi_fused__softmax_mul_squeeze_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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__softmax_mul_squeeze_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_mul_squeeze_sum_1(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
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp14 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp21 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tmp5 = tmp0 * tmp4
    tmp8 = tmp7 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp9 / tmp9
    tmp11 = tmp6 * tmp10
    tmp12 = tmp5 + tmp11
    tmp15 = tmp14 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 / tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp12 + tmp18
    tmp22 = tmp21 - tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp23 / tmp23
    tmp25 = tmp20 * tmp24
    tmp26 = tmp19 + tmp25
    tl.store(out_ptr0 + (x2), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xp/cxp3ouwpdhdlmipppq44wjaey2obmthzec7uqoddmpoigfmupxdx.py
# Topologically Sorted Source Nodes: [left_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   left_1 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_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, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', '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_2(in_ptr0, out_ptr0, 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
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/jm/cjm36trdlg72omrr7zkym7yxeaacsnhte4jsdswsq4c4nrutktgo.py
# Topologically Sorted Source Nodes: [left_1, add, H_1], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   H_1 => tanh_1
#   add => add_1
#   left_1 => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_5, %primals_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %unsqueeze), kwargs = {})
#   %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_tanh_3 = async_compile.triton('triton_poi_fused_add_tanh_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: '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_add_tanh_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_tanh_3(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_4/inductor_cache/ay/cayex55l254rwdhzmrfg7dhhw7pjnjb7q33p4lmi7jqxc3cywyld.py
# Topologically Sorted Source Nodes: [a_4, a_5, a_6, attention_feat_vis], Original ATen: [aten._softmax, aten.mul, aten.sum, aten.squeeze]
# Source node to ATen node mapping:
#   a_4 => amax_1, div_1, exp_1, sub_1, sum_3
#   a_5 => mul_1
#   a_6 => sum_4
#   attention_feat_vis => squeeze_1
# Graph fragment:
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_7, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_7, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_3 : [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_3), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute, %div_1), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
#   %squeeze_1 : [num_users=2] = call_function[target=torch.ops.aten.squeeze.default](args = (%sum_4,), kwargs = {})
triton_poi_fused__softmax_mul_squeeze_sum_4 = async_compile.triton('triton_poi_fused__softmax_mul_squeeze_sum_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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__softmax_mul_squeeze_sum_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_mul_squeeze_sum_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tmp5 = tmp0 * tmp4
    tmp8 = tmp7 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp9 / tmp9
    tmp11 = tmp6 * tmp10
    tmp12 = tmp5 + tmp11
    tmp15 = tmp14 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 / tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp12 + tmp18
    tmp22 = tmp21 - tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp23 / tmp23
    tmp25 = tmp20 * tmp24
    tmp26 = tmp19 + tmp25
    tl.store(out_ptr0 + (x2), tmp26, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (1, ), (1, ))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [H], Original ATen: [aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_tanh_0.run(buf0, primals_3, buf1, 64, grid=grid(64), stream=stream0)
        buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [res], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [a, a_1, a_2, a_3], Original ATen: [aten._softmax, aten.mul, aten.sum, aten.squeeze]
        triton_poi_fused__softmax_mul_squeeze_sum_1.run(primals_4, buf3, buf4, 16, grid=grid(16), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [left_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(primals_1, buf5, 16, 4, grid=grid(16, 4), stream=stream0)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [left_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf6)
        del primals_2
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf4, reinterpret_tensor(primals_7, (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: [left_1, add, H_1], Original ATen: [aten.add, aten.tanh]
        triton_poi_fused_add_tanh_3.run(buf8, primals_3, buf7, primals_8, 64, grid=grid(64), stream=stream0)
        buf10 = reinterpret_tensor(buf7, (16, 1), (1, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [res_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf10)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [a_4, a_5, a_6, attention_feat_vis], Original ATen: [aten._softmax, aten.mul, aten.sum, aten.squeeze]
        triton_poi_fused__softmax_mul_squeeze_sum_4.run(primals_1, buf10, buf11, 16, grid=grid(16), stream=stream0)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf11, reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf12)
        buf13 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [add_1, H_2], Original ATen: [aten.add, aten.tanh]
        triton_poi_fused_add_tanh_3.run(buf13, primals_3, buf12, primals_8, 64, grid=grid(64), stream=stream0)
        del primals_3
        del primals_8
        buf15 = reinterpret_tensor(buf12, (16, 1), (1, 1), 0); del buf12  # reuse
        # Topologically Sorted Source Nodes: [res_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf13, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf15)
        del primals_6
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [a_7, a_8, a_9, attention_feat_ques], Original ATen: [aten._softmax, aten.mul, aten.sum, aten.squeeze]
        triton_poi_fused__softmax_mul_squeeze_sum_1.run(primals_4, buf15, buf16, 16, grid=grid(16), stream=stream0)
    return (buf11, buf16, primals_1, primals_4, buf1, buf3, buf4, reinterpret_tensor(buf5, (16, 4), (4, 1), 0), buf8, buf10, buf11, buf13, buf15, primals_5, primals_7, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, ), (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)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    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


class AlternateAttention(nn.Module):

    def __init__(self, embedding_size, hidden_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.embedding_size = embedding_size
        self.x_linear = nn.Linear(self.embedding_size, self.hidden_size)
        self.g_linear = nn.Linear(self.embedding_size, self.hidden_size)
        self.linear_t = nn.Linear(self.hidden_size, 1)
        self.activation = nn.Tanh()
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, ques_embed_t, img_embed):
        img_embed_t = img_embed.permute(0, 2, 1)
        left = self.x_linear(ques_embed_t)
        H = self.activation(left)
        res = self.linear_t(H)
        a = self.softmax(res)
        a = torch.mul(ques_embed_t, a)
        a = torch.sum(a, dim=1)
        a = a.squeeze()
        left = self.x_linear(img_embed_t)
        right = self.g_linear(a)
        right = right.unsqueeze(1)
        H = self.activation(left + right)
        res = self.linear_t(H)
        a = self.softmax(res)
        a = torch.mul(img_embed_t, a)
        a = torch.sum(a, dim=1)
        attention_feat_vis = a.squeeze()
        left = self.x_linear(ques_embed_t)
        right = self.g_linear(attention_feat_vis)
        right = right.unsqueeze(1)
        H = self.activation(left + right)
        res = self.linear_t(H)
        a = self.softmax(res)
        a = torch.mul(ques_embed_t, a)
        a = torch.sum(a, dim=1)
        attention_feat_ques = a.squeeze()
        return attention_feat_vis, attention_feat_ques


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


def get_init_inputs():
    return [[], {'embedding_size': 4, 'hidden_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.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_tanh_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
    tmp3 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_squeeze_sum_1(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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tmp5 = tmp0 * tmp4
    tmp8 = tmp7 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp9 / tmp9
    tmp11 = tmp6 * tmp10
    tmp12 = tmp5 + tmp11
    tmp15 = tmp14 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 / tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp12 + tmp18
    tmp22 = tmp21 - tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp23 / tmp23
    tmp25 = tmp20 * tmp24
    tmp26 = tmp19 + tmp25
    tl.store(out_ptr0 + x2, tmp26, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, 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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_tanh_3(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_mul_squeeze_sum_4(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tmp5 = tmp0 * tmp4
    tmp8 = tmp7 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp9 / tmp9
    tmp11 = tmp6 * tmp10
    tmp12 = tmp5 + tmp11
    tmp15 = tmp14 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 / tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp12 + tmp18
    tmp22 = tmp21 - tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp23 / tmp23
    tmp25 = tmp20 * tmp24
    tmp26 = tmp19 + tmp25
    tl.store(out_ptr0 + x2, tmp26, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (1,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(64)](buf0, primals_3, buf1, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf1, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_mul_squeeze_sum_1[grid(16)](primals_4,
            buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 4)](primals_1, buf5, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf6)
        del primals_2
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_7, (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_3[grid(64)](buf8, primals_3, buf7,
            primals_8, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf7, (16, 1), (1, 1), 0)
        del buf7
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf8, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf10)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_mul_squeeze_sum_4[grid(16)](primals_1,
            buf10, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf11, reinterpret_tensor(primals_7, (4, 4), (1, 
            4), 0), out=buf12)
        buf13 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_tanh_3[grid(64)](buf13, primals_3, buf12,
            primals_8, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        del primals_8
        buf15 = reinterpret_tensor(buf12, (16, 1), (1, 1), 0)
        del buf12
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf13, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf15)
        del primals_6
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_mul_squeeze_sum_1[grid(16)](primals_4,
            buf15, buf16, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return (buf11, buf16, primals_1, primals_4, buf1, buf3, buf4,
        reinterpret_tensor(buf5, (16, 4), (4, 1), 0), buf8, buf10, buf11,
        buf13, buf15, primals_5, primals_7)


class AlternateAttentionNew(nn.Module):

    def __init__(self, embedding_size, hidden_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.embedding_size = embedding_size
        self.x_linear = nn.Linear(self.embedding_size, self.hidden_size)
        self.g_linear = nn.Linear(self.embedding_size, self.hidden_size)
        self.linear_t = nn.Linear(self.hidden_size, 1)
        self.activation = nn.Tanh()
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, input_0, input_1):
        primals_2 = self.x_linear.weight
        primals_3 = self.x_linear.bias
        primals_7 = self.g_linear.weight
        primals_8 = self.g_linear.bias
        primals_5 = self.linear_t.weight
        primals_6 = self.linear_t.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0], output[1]
