# 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/vy/cvy6gnjnn3e26xiv5mvezsj6i3mtno74nl7nnw2mynr5tyqzleaw.py
# Topologically Sorted Source Nodes: [squeeze], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   squeeze => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2, %primals_3, %primals_4], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x1) + ((-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_ptr2 + ((4*x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr3 + ((4*x1) + ((-12) + x0)), tmp16 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + (x2), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/kr/ckrtz7euvwxqnkg5oephcyx6h3igdbclxrev67sp4jowqno2d5nz.py
# Topologically Sorted Source Nodes: [excitation_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   excitation_1 => relu
# Graph fragment:
#   %add_tensor_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_3, %primals_6), kwargs = {})
#   %relu : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_3,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_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=[32], 
    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_relu_1', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/uz/cuza4el6rvyijq4dcndmh5x73aprylny3aerql5hqk6c5zzefudx.py
# Topologically Sorted Source Nodes: [img_out_scale1_1, mul_1], Original ATen: [aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   img_out_scale1_1 => sigmoid_1
#   mul_1 => mul_1
# Graph fragment:
#   %add_tensor_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_10), kwargs = {})
#   %sigmoid_1 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_tensor_2,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %sigmoid_1), kwargs = {})
triton_poi_fused_mul_sigmoid_2 = async_compile.triton('triton_poi_fused_mul_sigmoid_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: '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_mul_sigmoid_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_sigmoid_2(in_out_ptr0, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp4 * tmp3
    tl.store(in_out_ptr0 + (x2), tmp3, xmask)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/55/c55o3axbbgol2pbdxrfpudcc7j6daijsn6ynnru6chxpbflm5fsu.py
# Topologically Sorted Source Nodes: [img_out_scale2_1, img_out_scale3_1, mul_2, mul_3], Original ATen: [aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   img_out_scale2_1 => sigmoid_2
#   img_out_scale3_1 => sigmoid_3
#   mul_2 => mul_2
#   mul_3 => mul_3
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_12), kwargs = {})
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_14), kwargs = {})
#   %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_tensor_1,), kwargs = {})
#   %sigmoid_3 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_tensor,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %sigmoid_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %sigmoid_3), kwargs = {})
triton_poi_fused_mul_sigmoid_3 = async_compile.triton('triton_poi_fused_mul_sigmoid_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 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_mul_sigmoid_3', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], '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_mul_sigmoid_3(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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')
    tmp4 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp5 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp9 = tmp8 * tmp7
    tmp10 = tmp8 * tmp3
    tl.store(in_out_ptr0 + (x2), tmp3, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, xmask)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
    tl.store(out_ptr1 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cj/ccj3dmk4yt5jnhs2y3xl3nmbns7i5y655icfkjauxekfy5jilzkz.py
# Topologically Sorted Source Nodes: [tab_out_1, mul], Original ATen: [aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   mul => mul
#   tab_out_1 => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %sigmoid), kwargs = {})
triton_poi_fused_mul_sigmoid_4 = async_compile.triton('triton_poi_fused_mul_sigmoid_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_mul_sigmoid_4', '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_sigmoid_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (8, 16), (16, 1))
    assert_size_stride(primals_6, (8, ), (1, ))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 8), (8, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, 8), (8, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, 8), (8, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [squeeze], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, primals_3, primals_4, buf0, 64, grid=grid(64), stream=stream0)
        del primals_4
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf0, reinterpret_tensor(primals_5, (16, 8), (1, 16), 0), out=buf1)
        del primals_5
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [excitation_1], Original ATen: [aten.relu]
        triton_poi_fused_relu_1.run(buf2, primals_6, 32, grid=grid(32), stream=stream0)
        del primals_6
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tab_out], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, buf2, reinterpret_tensor(primals_7, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf3)
        del primals_8
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_9, (8, 4), (1, 8), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_11, (8, 4), (1, 8), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_13, (8, 4), (1, 8), 0), out=buf6)
        buf7 = buf4; del buf4  # reuse
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [img_out_scale1_1, mul_1], Original ATen: [aten.sigmoid, aten.mul]
        triton_poi_fused_mul_sigmoid_2.run(buf7, primals_10, primals_2, buf11, 16, grid=grid(16), stream=stream0)
        del primals_10
        buf9 = buf6; del buf6  # reuse
        buf8 = buf5; del buf5  # reuse
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [img_out_scale2_1, img_out_scale3_1, mul_2, mul_3], Original ATen: [aten.sigmoid, aten.mul]
        triton_poi_fused_mul_sigmoid_3.run(buf9, buf8, primals_14, primals_12, primals_3, buf12, buf13, 16, grid=grid(16), stream=stream0)
        del primals_12
        del primals_14
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tab_out_1, mul], Original ATen: [aten.sigmoid, aten.mul]
        triton_poi_fused_mul_sigmoid_4.run(primals_1, buf3, buf10, 16, grid=grid(16), stream=stream0)
    return (buf10, buf11, buf7, buf12, buf8, buf13, buf9, primals_1, primals_2, primals_3, buf0, buf2, buf3, buf7, buf8, buf9, primals_13, primals_11, primals_9, 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, 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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = 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, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
from typing import Sequence


class MMTMQuad(nn.Module):
    """
    quad modal fusion
    """

    def __init__(self, dim_tab, dim_img, ratio=4):
        """

        Parameters
        ----------
        dim_tab: feature dimension of tabular data
        dim_img: feature dimension of MIL model
        ratio
        """
        super(MMTMQuad, self).__init__()
        dim = dim_tab + dim_img * 3
        dim_out = int(2 * dim / ratio)
        self.fc_squeeze = nn.Linear(dim, dim_out)
        self.fc_tab = nn.Linear(dim_out, dim_tab)
        self.fc_img_scale1 = nn.Linear(dim_out, dim_img)
        self.fc_img_scale2 = nn.Linear(dim_out, dim_img)
        self.fc_img_scale3 = nn.Linear(dim_out, dim_img)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, tab_feat, img_feat_scale1, img_feat_scale2,
        img_feat_scale3) ->Sequence[torch.Tensor]:
        """

        Parameters
        ----------
        tab_feat: b * c
        skeleton: b * c

        Returns
        -------

        """
        squeeze = torch.cat([tab_feat, img_feat_scale1, img_feat_scale2,
            img_feat_scale3], dim=1)
        excitation = self.fc_squeeze(squeeze)
        excitation = self.relu(excitation)
        tab_out = self.fc_tab(excitation)
        img_out_scale1 = self.fc_img_scale1(excitation)
        img_out_scale2 = self.fc_img_scale2(excitation)
        img_out_scale3 = self.fc_img_scale3(excitation)
        tab_out = self.sigmoid(tab_out)
        img_out_scale1 = self.sigmoid(img_out_scale1)
        img_out_scale2 = self.sigmoid(img_out_scale2)
        img_out_scale3 = self.sigmoid(img_out_scale3)
        return (tab_feat * tab_out, img_feat_scale1 * img_out_scale1,
            img_out_scale1, img_feat_scale2 * img_out_scale2,
            img_out_scale2, img_feat_scale2 * img_out_scale3, img_out_scale3)


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


def get_init_inputs():
    return [[], {'dim_tab': 4, 'dim_img': 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
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-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_ptr2 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr3 + (4 * x1 + (-12 + x0)), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_2(in_out_ptr0, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp4 * tmp3
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_3(in_out_ptr0, in_out_ptr1, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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')
    tmp4 = tl.load(in_out_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp9 = tmp8 * tmp7
    tmp10 = tmp8 * tmp3
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (8, 16), (16, 1))
    assert_size_stride(primals_6, (8,), (1,))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 8), (8, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 8), (8, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4, 8), (8, 1))
    assert_size_stride(primals_14, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(64)](primals_1, primals_2, primals_3,
            primals_4, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_4
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_5, (16, 8), (1, 
            16), 0), out=buf1)
        del primals_5
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(32)](buf2, primals_6, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del primals_6
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf2, reinterpret_tensor(primals_7,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf3)
        del primals_8
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_9, (8, 4), (1, 8
            ), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_11, (8, 4), (1, 
            8), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_13, (8, 4), (1, 
            8), 0), out=buf6)
        buf7 = buf4
        del buf4
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_2[grid(16)](buf7, primals_10,
            primals_2, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_10
        buf9 = buf6
        del buf6
        buf8 = buf5
        del buf5
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_3[grid(16)](buf9, buf8, primals_14,
            primals_12, primals_3, buf12, buf13, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_12
        del primals_14
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_4[grid(16)](primals_1, buf3, buf10, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
    return (buf10, buf11, buf7, buf12, buf8, buf13, buf9, primals_1,
        primals_2, primals_3, buf0, buf2, buf3, buf7, buf8, buf9,
        primals_13, primals_11, primals_9, primals_7)


class MMTMQuadNew(nn.Module):
    """
    quad modal fusion
    """

    def __init__(self, dim_tab, dim_img, ratio=4):
        """

        Parameters
        ----------
        dim_tab: feature dimension of tabular data
        dim_img: feature dimension of MIL model
        ratio
        """
        super(MMTMQuadNew, self).__init__()
        dim = dim_tab + dim_img * 3
        dim_out = int(2 * dim / ratio)
        self.fc_squeeze = nn.Linear(dim, dim_out)
        self.fc_tab = nn.Linear(dim_out, dim_tab)
        self.fc_img_scale1 = nn.Linear(dim_out, dim_img)
        self.fc_img_scale2 = nn.Linear(dim_out, dim_img)
        self.fc_img_scale3 = nn.Linear(dim_out, dim_img)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_0, input_1, input_2, input_3):
        primals_5 = self.fc_squeeze.weight
        primals_6 = self.fc_squeeze.bias
        primals_7 = self.fc_tab.weight
        primals_8 = self.fc_tab.bias
        primals_9 = self.fc_img_scale1.weight
        primals_10 = self.fc_img_scale1.bias
        primals_11 = self.fc_img_scale2.weight
        primals_12 = self.fc_img_scale2.bias
        primals_13 = self.fc_img_scale3.weight
        primals_14 = self.fc_img_scale3.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        primals_4 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0], output[1], output[2], output[3], output[4], output[5
            ], output[6]
