# 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/ca/ccamaftwec2ej4cv3xm7b7hjfj4w6sc54ohvrqstpfcjdv5dt4wk.py
# Topologically Sorted Source Nodes: [sub, mul, alpha_1, alphas], Original ATen: [aten.rsub, aten.mul, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   alpha_1 => add
#   alphas => amax, exp, sub_1, sum_1
#   mul => mul
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %unsqueeze), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -1e+30), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %mul), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__softmax_add_mul_rsub_0 = async_compile.triton('triton_poi_fused__softmax_add_mul_rsub_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_mul_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp9 = tmp2 - tmp8
    tmp10 = tmp9 * tmp4
    tmp11 = tmp7 + tmp10
    tmp12 = triton_helpers.maximum(tmp6, tmp11)
    tmp15 = tmp2 - tmp14
    tmp16 = tmp15 * tmp4
    tmp17 = tmp13 + tmp16
    tmp18 = triton_helpers.maximum(tmp12, tmp17)
    tmp21 = tmp2 - tmp20
    tmp22 = tmp21 * tmp4
    tmp23 = tmp19 + tmp22
    tmp24 = triton_helpers.maximum(tmp18, tmp23)
    tmp25 = tmp6 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp11 - tmp24
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tmp30 = tmp17 - tmp24
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp29 + tmp31
    tmp33 = tmp23 - tmp24
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp32 + tmp34
    tl.store(out_ptr0 + (x0), tmp24, xmask)
    tl.store(out_ptr1 + (x0), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/2a/c2a4a3verzjc7fofh2ji4x5l4lfzabvbshkctgcsugurkvmr352b.py
# Topologically Sorted Source Nodes: [sub, mul, alpha_1, alphas], Original ATen: [aten.rsub, aten.mul, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   alpha_1 => add
#   alphas => amax, div, exp, sub_1
#   mul => mul
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %unsqueeze), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -1e+30), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %mul), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_add_mul_rsub_1 = async_compile.triton('triton_poi_fused__softmax_add_mul_rsub_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: '*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__softmax_add_mul_rsub_1', '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__softmax_add_mul_rsub_1(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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x2), xmask)
    tmp7 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 / tmp10
    tl.store(in_out_ptr0 + (x2), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/zh/czhivwf6z2rlnzniqfcwfrzuuj7oubiwqsip5gr6nkhxxbr3hvvq.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   output => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_4, %repeat], 2), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x3 = (xindex // 8)
    x2 = (xindex // 32)
    x4 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x3) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x2) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x4), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/b4/cb4g5d2wucilck3cceyt7mv54jpk37vi2vdxss3fuxkao2dxhkaz.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_1 => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute_3, %primals_5, %primals_6, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32, 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=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_convolution_3', '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_convolution_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 32
    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 % 8
    y1 = (yindex // 8)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (8*x2) + (32*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_1/inductor_cache/ui/cui7jie2ijke3c32epkgfdidx5cx7xc2wpxhivrclahyi6dg6uxt.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_1 => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute_3, %primals_5, %primals_6, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_4 = async_compile.triton('triton_poi_fused_convolution_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=[64], 
    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_convolution_4', '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_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 8, 1), (8, 1, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alpha], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf2 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [sub, mul, alpha_1, alphas], Original ATen: [aten.rsub, aten.mul, aten.add, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_add_mul_rsub_0.run(buf0, primals_3, buf1, buf2, 4, grid=grid(4), stream=stream0)
        buf3 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [sub, mul, alpha_1, alphas], Original ATen: [aten.rsub, aten.mul, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_mul_rsub_1.run(buf3, primals_3, buf1, buf2, 16, grid=grid(16), stream=stream0)
        del buf1
        del buf2
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pooled_x], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0), buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(primals_4, buf4, buf5, 128, grid=grid(128), stream=stream0)
        del buf4
        del primals_4
        buf6 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf5, buf6, 32, 4, grid=grid(32, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_5, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4), (16, 4, 1))
        del buf6
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf8, primals_6, 64, grid=grid(64), stream=stream0)
        del primals_6
    return (reinterpret_tensor(buf8, (4, 4, 4), (16, 1, 4), 0), primals_2, primals_5, buf3, reinterpret_tensor(buf5, (4, 8, 4), (32, 1, 8), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (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((4, 8, 1), (8, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = 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])
    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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn


def mask_logits(inputs, mask, mask_value=-1e+30):
    mask = mask.type(torch.float32)
    return inputs + (1.0 - mask) * mask_value


class Conv1D(nn.Module):

    def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
        bias=True):
        super(Conv1D, self).__init__()
        self.conv1d = nn.Conv1d(in_channels=in_dim, out_channels=out_dim,
            kernel_size=kernel_size, padding=padding, stride=stride, bias=bias)

    def forward(self, x):
        x = x.transpose(1, 2)
        x = self.conv1d(x)
        return x.transpose(1, 2)


class WeightedPool(nn.Module):

    def __init__(self, dim):
        super(WeightedPool, self).__init__()
        weight = torch.empty(dim, 1)
        nn.init.xavier_uniform_(weight)
        self.weight = nn.Parameter(weight, requires_grad=True)

    def forward(self, x, mask):
        alpha = torch.tensordot(x, self.weight, dims=1)
        alpha = mask_logits(alpha, mask=mask.unsqueeze(2))
        alphas = nn.Softmax(dim=1)(alpha)
        pooled_x = torch.matmul(x.transpose(1, 2), alphas)
        pooled_x = pooled_x.squeeze(2)
        return pooled_x


class CQConcatenate(nn.Module):

    def __init__(self, dim):
        super(CQConcatenate, self).__init__()
        self.weighted_pool = WeightedPool(dim=dim)
        self.conv1d = Conv1D(in_dim=2 * dim, out_dim=dim, kernel_size=1,
            stride=1, padding=0, bias=True)

    def forward(self, context, query, q_mask):
        pooled_query = self.weighted_pool(query, q_mask)
        _, c_seq_len, _ = context.shape
        pooled_query = pooled_query.unsqueeze(1).repeat(1, c_seq_len, 1)
        output = torch.cat([context, pooled_query], dim=2)
        output = self.conv1d(output)
        return output


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


def get_init_inputs():
    return [[], {'dim': 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 math as tl_math
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
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__softmax_add_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp9 = tmp2 - tmp8
    tmp10 = tmp9 * tmp4
    tmp11 = tmp7 + tmp10
    tmp12 = triton_helpers.maximum(tmp6, tmp11)
    tmp15 = tmp2 - tmp14
    tmp16 = tmp15 * tmp4
    tmp17 = tmp13 + tmp16
    tmp18 = triton_helpers.maximum(tmp12, tmp17)
    tmp21 = tmp2 - tmp20
    tmp22 = tmp21 * tmp4
    tmp23 = tmp19 + tmp22
    tmp24 = triton_helpers.maximum(tmp18, tmp23)
    tmp25 = tmp6 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp11 - tmp24
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tmp30 = tmp17 - tmp24
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp29 + tmp31
    tmp33 = tmp23 - tmp24
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp32 + tmp34
    tl.store(out_ptr0 + x0, tmp24, xmask)
    tl.store(out_ptr1 + x0, tmp35, xmask)


@triton.jit
def triton_poi_fused__softmax_add_mul_rsub_1(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
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x2, xmask)
    tmp7 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 / tmp10
    tl.store(in_out_ptr0 + x2, tmp11, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x3 = xindex // 8
    x2 = xindex // 32
    x4 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x3 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x2 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x4, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_3(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 32
    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 % 8
    y1 = yindex // 8
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 8 * x2 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 8, 1), (8, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf2 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_add_mul_rsub_0[grid(4)](buf0, primals_3,
            buf1, buf2, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0)
        del buf0
        triton_poi_fused__softmax_add_mul_rsub_1[grid(16)](buf3, primals_3,
            buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf1
        del buf2
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 4, 4), (16, 1,
            4), 0), buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_2[grid(128)](primals_4, buf4, buf5, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        del primals_4
        buf6 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        triton_poi_fused_convolution_3[grid(32, 4)](buf5, buf6, 32, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf7 = extern_kernels.convolution(buf6, primals_5, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4), (16, 4, 1))
        del buf6
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_4[grid(64)](buf8, primals_6, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_6
    return reinterpret_tensor(buf8, (4, 4, 4), (16, 1, 4), 0
        ), primals_2, primals_5, buf3, reinterpret_tensor(buf5, (4, 8, 4),
        (32, 1, 8), 0)


def mask_logits(inputs, mask, mask_value=-1e+30):
    mask = mask.type(torch.float32)
    return inputs + (1.0 - mask) * mask_value


class Conv1D(nn.Module):

    def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
        bias=True):
        super(Conv1D, self).__init__()
        self.conv1d = nn.Conv1d(in_channels=in_dim, out_channels=out_dim,
            kernel_size=kernel_size, padding=padding, stride=stride, bias=bias)

    def forward(self, x):
        x = x.transpose(1, 2)
        x = self.conv1d(x)
        return x.transpose(1, 2)


class WeightedPool(nn.Module):

    def __init__(self, dim):
        super(WeightedPool, self).__init__()
        weight = torch.empty(dim, 1)
        nn.init.xavier_uniform_(weight)
        self.weight = nn.Parameter(weight, requires_grad=True)

    def forward(self, x, mask):
        alpha = torch.tensordot(x, self.weight, dims=1)
        alpha = mask_logits(alpha, mask=mask.unsqueeze(2))
        alphas = nn.Softmax(dim=1)(alpha)
        pooled_x = torch.matmul(x.transpose(1, 2), alphas)
        pooled_x = pooled_x.squeeze(2)
        return pooled_x


class CQConcatenateNew(nn.Module):

    def __init__(self, dim):
        super(CQConcatenateNew, self).__init__()
        self.weighted_pool = WeightedPool(dim=dim)
        self.conv1d = Conv1D(in_dim=2 * dim, out_dim=dim, kernel_size=1,
            stride=1, padding=0, bias=True)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.weighted_pool.weight
        primals_5 = self.conv1d.conv1d.weight
        primals_6 = self.conv1d.conv1d.bias
        primals_2 = input_0
        primals_4 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
