# 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/gv/cgvyzvgb4s6skjl2lcdf54y4sqcmmvdkvv2hcpobs5hraiugivrp.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone, aten._unsafe_view]
# Source node to ATen node mapping:
#   matmul => clone_2, view
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
#   %view : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%clone_2, [16, 4]), kwargs = {})
triton_poi_fused__unsafe_view_clone_0 = async_compile.triton('triton_poi_fused__unsafe_view_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16, 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__unsafe_view_clone_0', '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__unsafe_view_clone_0(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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + ((4*x1) + (16*(y0 // 4)) + (y0 % 4)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pa/cpa2xlx5dvbxg7yen7xxu7aclanjppmhz3pxswu4bw5tkkqjh7rr.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute, %primals_5), kwargs = {})
triton_poi_fused_mul_1 = async_compile.triton('triton_poi_fused_mul_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_1', '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_1(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
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/uf/cufxi6e4u5mvvfd5sut4qxvbrhzb7usdtxtg3ze2t6ajs3np5275.py
# Topologically Sorted Source Nodes: [add, res, res_1, mul_1, sub, mul_2, add_2, mul_3, sub_1, mul_4, add_3], Original ATen: [aten.add, aten.mul, aten.rsub]
# Source node to ATen node mapping:
#   add => add
#   add_2 => add_3
#   add_3 => add_4
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   res => add_1
#   res_1 => add_2
#   sub => sub
#   sub_1 => sub_2
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, %expand_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %bmm), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %primals_6), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, %primals_8), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %primals_8), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -1e+30), kwargs = {})
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, %primals_7), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %primals_7), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, -1e+30), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %mul_4), kwargs = {})
triton_poi_fused_add_mul_rsub_2 = async_compile.triton('triton_poi_fused_add_mul_rsub_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '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, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_rsub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = (xindex // 4)
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4), xmask)
    tmp5 = tl.load(in_ptr3 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp8 = tl.load(in_ptr4 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr5 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp7 = tmp4 + tmp6
    tmp9 = tmp7 * tmp8
    tmp10 = 1.0
    tmp11 = tmp10 - tmp8
    tmp12 = -1e+30
    tmp13 = tmp11 * tmp12
    tmp14 = tmp9 + tmp13
    tmp16 = tmp7 * tmp15
    tmp17 = tmp10 - tmp15
    tmp18 = tmp17 * tmp12
    tmp19 = tmp16 + tmp18
    tl.store(out_ptr0 + (x4), tmp14, xmask)
    tl.store(out_ptr1 + (x4), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7s/c7spagnqvsgjrukyw5jujzjmswxuigeuvpyhxgdob766q2gfvgzr.py
# Topologically Sorted Source Nodes: [S1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   S1 => amax, exp, sub_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_3, [2], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, 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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dw/cdwqsjnh2osfmjr2utzzaqdg2vrfivzkuhareq3urgidllj2bsvr.py
# Topologically Sorted Source Nodes: [S1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   S1 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
#   %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5q/c5q2cwpxtjxxz7h6xna43qv2cdyea56heflv2ye7d7mtmtdm7twa.py
# Topologically Sorted Source Nodes: [S2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   S2 => amax_1, exp_1, sub_3
# Graph fragment:
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_4, [1], True), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_4, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), kwargs = {})
triton_poi_fused__softmax_5 = async_compile.triton('triton_poi_fused__softmax_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 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_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cg/ccg5e776j77ye72qtmo5nfcxjaz6zv34474xpm34f62r6hfxzo6g.py
# Topologically Sorted Source Nodes: [S2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   S2 => div_1, sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), kwargs = {})
triton_poi_fused__softmax_6 = async_compile.triton('triton_poi_fused__softmax_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_6(in_ptr0, out_ptr0, 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_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pj/cpjglqinm2mgqqclt3c66vcfcnwohgtuw2thbq5rdw75hhx4fn5r.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%permute, %bmm_1, %mul_5, %mul_6], 2), kwargs = {})
triton_poi_fused_cat_7 = async_compile.triton('triton_poi_fused_cat_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_7(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16) % 4
    x2 = (xindex // 64)
    x3 = (xindex // 16)
    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 + (x1 + (4*x0) + (16*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x3) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (x1 + (4*((-8) + x0)) + (16*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + ((4*x3) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 * tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tmp21 = tl.full([1], 16, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tl.load(in_ptr0 + (x1 + (4*((-12) + x0)) + (16*x2)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr2 + ((4*x3) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + (x4), tmp30, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, 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), (16, 4, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_6, (1, ), (1, ))
    assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_8, (4, 1, 4), (4, 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: [matmul], Original ATen: [aten.clone, aten._unsafe_view]
        stream0 = get_raw_stream(0)
        triton_poi_fused__unsafe_view_clone_0.run(primals_1, buf0, 16, 4, grid=grid(16, 4), stream=stream0)
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.clone, aten._unsafe_view]
        triton_poi_fused__unsafe_view_clone_0.run(primals_2, buf2, 16, 4, grid=grid(16, 4), stream=stream0)
        buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf2, primals_4, out=buf3)
        del primals_4
        buf4 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        triton_poi_fused_mul_1.run(primals_1, primals_5, buf4, 64, grid=grid(64), stream=stream0)
        del primals_5
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, subres2], Original ATen: [aten.mul, aten.bmm]
        extern_kernels.bmm(buf4, primals_2, out=buf5)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0); del buf4  # reuse
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, res, res_1, mul_1, sub, mul_2, add_2, mul_3, sub_1, mul_4, add_3], Original ATen: [aten.add, aten.mul, aten.rsub]
        triton_poi_fused_add_mul_rsub_2.run(buf1, buf3, buf5, primals_6, primals_8, primals_7, buf6, buf9, 64, grid=grid(64), stream=stream0)
        del buf1
        del buf3
        del primals_6
        buf7 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [S1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf6, buf7, 64, grid=grid(64), stream=stream0)
        buf8 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [S1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf7, buf8, 64, grid=grid(64), stream=stream0)
        buf10 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [S2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_5.run(buf9, buf10, 64, grid=grid(64), stream=stream0)
        buf11 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [S2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_6.run(buf10, buf11, 64, grid=grid(64), stream=stream0)
        buf12 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [A], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf8, reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bmm_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf8, reinterpret_tensor(buf11, (4, 4, 4), (16, 1, 4), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [B], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf13, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), out=buf14)
        del buf13
        buf15 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
        triton_poi_fused_cat_7.run(primals_1, buf12, buf14, buf15, 256, grid=grid(256), stream=stream0)
        del buf12
        del buf14
    return (reinterpret_tensor(buf15, (4, 16, 4), (64, 1, 16), 0), primals_7, primals_8, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), primals_2, buf8, buf11, reinterpret_tensor(buf2, (4, 16), (1, 4), 0), reinterpret_tensor(buf0, (4, 16), (1, 4), 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, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 1, 4), (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, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 1, 4), (4, 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
import torch.nn.functional as F


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


class CQAttention(nn.Module):

    def __init__(self, d_model, dropout=0.1):
        super().__init__()
        w4C = torch.empty(d_model, 1)
        w4Q = torch.empty(d_model, 1)
        w4mlu = torch.empty(1, 1, d_model)
        nn.init.xavier_uniform_(w4C)
        nn.init.xavier_uniform_(w4Q)
        nn.init.xavier_uniform_(w4mlu)
        self.w4C = nn.Parameter(w4C)
        self.w4Q = nn.Parameter(w4Q)
        self.w4mlu = nn.Parameter(w4mlu)
        bias = torch.empty(1)
        nn.init.constant_(bias, 0)
        self.bias = nn.Parameter(bias)
        self.dropout = dropout

    def forward(self, C, Q, Cmask, Qmask):
        C = C.transpose(1, 2)
        Q = Q.transpose(1, 2)
        batch_size_c = C.size()[0]
        _batch_size, Lc, _d_model = C.shape
        _batch_size, Lq, _d_model = Q.shape
        S = self.trilinear_for_attention(C, Q)
        Cmask = Cmask.view(batch_size_c, Lc, 1)
        Qmask = Qmask.view(batch_size_c, 1, Lq)
        S1 = F.softmax(mask_logits(S, Qmask), dim=2)
        S2 = F.softmax(mask_logits(S, Cmask), dim=1)
        A = torch.bmm(S1, Q)
        B = torch.bmm(torch.bmm(S1, S2.transpose(1, 2)), C)
        out = torch.cat([C, A, torch.mul(C, A), torch.mul(C, B)], dim=2)
        return out.transpose(1, 2)

    def trilinear_for_attention(self, C, Q):
        _batch_size, Lc, _d_model = C.shape
        _batch_size, Lq, _d_model = Q.shape
        dropout = self.dropout
        C = F.dropout(C, p=dropout, training=self.training)
        Q = F.dropout(Q, p=dropout, training=self.training)
        subres0 = torch.matmul(C, self.w4C).expand([-1, -1, Lq])
        subres1 = torch.matmul(Q, self.w4Q).transpose(1, 2).expand([-1, Lc, -1]
            )
        subres2 = torch.matmul(C * self.w4mlu, Q.transpose(1, 2))
        res = subres0 + subres1 + subres2
        res += self.bias
        return res


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4,
        1]), torch.rand([4, 1, 4])]


def get_init_inputs():
    return [[], {'d_model': 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 as nn
import torch.nn.functional as F
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__unsafe_view_clone_0(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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + 16 * (y0 // 4) + y0 % 4), xmask &
        ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_mul_1(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
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, in_ptr5, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr2 + x4, xmask)
    tmp5 = tl.load(in_ptr3 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp8 = tl.load(in_ptr4 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr5 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp7 = tmp4 + tmp6
    tmp9 = tmp7 * tmp8
    tmp10 = 1.0
    tmp11 = tmp10 - tmp8
    tmp12 = -1e+30
    tmp13 = tmp11 * tmp12
    tmp14 = tmp9 + tmp13
    tmp16 = tmp7 * tmp15
    tmp17 = tmp10 - tmp15
    tmp18 = tmp17 * tmp12
    tmp19 = tmp16 + tmp18
    tl.store(out_ptr0 + x4, tmp14, xmask)
    tl.store(out_ptr1 + x4, tmp19, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, out_ptr0, 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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_6(in_ptr0, out_ptr0, 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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_7(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16 % 4
    x2 = xindex // 64
    x3 = xindex // 16
    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 + (x1 + 4 * x0 + 16 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x3 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (x1 + 4 * (-8 + x0) + 16 * x2), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + (4 * x3 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 * tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp23 = tl.load(in_ptr0 + (x1 + 4 * (-12 + x0) + 16 * x2), tmp20 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr2 + (4 * x3 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + x4, tmp30, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, 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), (16, 4, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_6, (1,), (1,))
    assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_8, (4, 1, 4), (4, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_view_clone_0[grid(16, 4)](primals_1, buf0,
            16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf0, primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        triton_poi_fused__unsafe_view_clone_0[grid(16, 4)](primals_2, buf2,
            16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf2, primals_4, out=buf3)
        del primals_4
        buf4 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        triton_poi_fused_mul_1[grid(64)](primals_1, primals_5, buf4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf4, primals_2, out=buf5)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
        del buf4
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_2[grid(64)](buf1, buf3, buf5,
            primals_6, primals_8, primals_7, buf6, buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf1
        del buf3
        del primals_6
        buf7 = buf5
        del buf5
        triton_poi_fused__softmax_3[grid(64)](buf6, buf7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf8 = buf6
        del buf6
        triton_poi_fused__softmax_4[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf10 = buf7
        del buf7
        triton_poi_fused__softmax_5[grid(64)](buf9, buf10, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf11 = buf9
        del buf9
        triton_poi_fused__softmax_6[grid(64)](buf10, buf11, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf12 = buf10
        del buf10
        extern_kernels.bmm(buf8, reinterpret_tensor(primals_2, (4, 4, 4), (
            16, 1, 4), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf8, reinterpret_tensor(buf11, (4, 4, 4), (16, 
            1, 4), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf13, reinterpret_tensor(primals_1, (4, 4, 4),
            (16, 1, 4), 0), out=buf14)
        del buf13
        buf15 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_cat_7[grid(256)](primals_1, buf12, buf14, buf15, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf12
        del buf14
    return reinterpret_tensor(buf15, (4, 16, 4), (64, 1, 16), 0
        ), primals_7, primals_8, reinterpret_tensor(primals_1, (4, 4, 4), (
        16, 1, 4), 0), primals_2, buf8, buf11, reinterpret_tensor(buf2, (4,
        16), (1, 4), 0), reinterpret_tensor(buf0, (4, 16), (1, 4), 0)


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


class CQAttentionNew(nn.Module):

    def __init__(self, d_model, dropout=0.1):
        super().__init__()
        w4C = torch.empty(d_model, 1)
        w4Q = torch.empty(d_model, 1)
        w4mlu = torch.empty(1, 1, d_model)
        nn.init.xavier_uniform_(w4C)
        nn.init.xavier_uniform_(w4Q)
        nn.init.xavier_uniform_(w4mlu)
        self.w4C = nn.Parameter(w4C)
        self.w4Q = nn.Parameter(w4Q)
        self.w4mlu = nn.Parameter(w4mlu)
        bias = torch.empty(1)
        nn.init.constant_(bias, 0)
        self.bias = nn.Parameter(bias)
        self.dropout = dropout

    def trilinear_for_attention(self, C, Q):
        _batch_size, Lc, _d_model = C.shape
        _batch_size, Lq, _d_model = Q.shape
        dropout = self.dropout
        C = F.dropout(C, p=dropout, training=self.training)
        Q = F.dropout(Q, p=dropout, training=self.training)
        subres0 = torch.matmul(C, self.w4C).expand([-1, -1, Lq])
        subres1 = torch.matmul(Q, self.w4Q).transpose(1, 2).expand([-1, Lc, -1]
            )
        subres2 = torch.matmul(C * self.w4mlu, Q.transpose(1, 2))
        res = subres0 + subres1 + subres2
        res += self.bias
        return res

    def forward(self, input_0, input_1, input_2, input_3):
        primals_3 = self.w4C
        primals_4 = self.w4Q
        primals_5 = self.w4mlu
        primals_6 = self.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_7 = input_2
        primals_8 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
