# 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/aw/cawte7o5l2qedgknpzshnd4zn5jxxhdbpkkqxowzkptjrvnqufl4.py
# Topologically Sorted Source Nodes: [logits_1, logits_2, squeeze], Original ATen: [aten.div, aten.add, aten.squeeze]
# Source node to ATen node mapping:
#   logits_1 => div
#   logits_2 => add
#   squeeze => squeeze
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_8, 1.4142135623730951), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_7), kwargs = {})
#   %squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.default](args = (%add,), kwargs = {})
triton_poi_fused_add_div_squeeze_0 = async_compile.triton('triton_poi_fused_add_div_squeeze_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=[256], 
    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_add_div_squeeze_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_squeeze_0(in_ptr0, in_ptr1, 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
    x2 = (xindex // 64)
    x3 = xindex % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = 0.7071067811865475
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + (x4), tmp4, 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 = args
    args.clear()
    assert_size_stride(primals_1, (2, 4), (4, 1))
    assert_size_stride(primals_2, (2, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (2, 4), (4, 1))
    assert_size_stride(primals_5, (2, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Q], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [K], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 2), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 2), (8, 2, 1), 0), reinterpret_tensor(buf1, (4, 2, 4), (8, 1, 2), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits_1, logits_2, squeeze], Original ATen: [aten.div, aten.add, aten.squeeze]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_squeeze_0.run(buf2, primals_7, buf3, 256, grid=grid(256), stream=stream0)
        del buf2
        del primals_7
    return (buf3, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 2, 4), (8, 1, 2), 0), reinterpret_tensor(buf1, (4, 4, 2), (8, 2, 1), 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((2, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((2, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4, 4), (16, 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])
    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)

from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch as th
from torchvision.ops.boxes import *
from torchvision.transforms.functional import *


class Grounding(nn.Module):

    def __init__(self, cfgT, cfgI, heads=1):
        super(Grounding, self).__init__()
        projection = cfgI.hidden_size // 2
        self.num_attention_heads = heads
        self.attention_head_size = int(projection // self.num_attention_heads)
        self.all_head_size = (self.num_attention_heads * self.
            attention_head_size)
        self.Q = nn.Linear(cfgT.hidden_size, self.all_head_size)
        self.K = nn.Linear(cfgI.hidden_size, self.all_head_size)
        self.cfgT = cfgT
        self.cfgI = cfgI

    def transpose(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.
            attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(self, encT, encI, mask):
        Q = self.Q(encT)
        K = self.K(encI)
        Q = self.transpose(Q)
        K = self.transpose(K)
        logits = th.matmul(Q, K.transpose(-1, -2))
        logits = logits / math.sqrt(self.attention_head_size)
        logits = logits + mask
        return logits.squeeze()


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


def get_init_inputs():
    return [[], {'cfgT': _mock_config(hidden_size=4), 'cfgI': _mock_config(
        hidden_size=4)}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.ops.boxes import *
from torchvision.transforms.functional import *
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_add_div_squeeze_0(in_ptr0, in_ptr1, 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
    x2 = xindex // 64
    x3 = xindex % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp1 = 0.7071067811865475
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + x4, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (2, 4), (4, 1))
    assert_size_stride(primals_2, (2,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (2, 4), (4, 1))
    assert_size_stride(primals_5, (2,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 2), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 2), (8, 2, 1), 0
            ), reinterpret_tensor(buf1, (4, 2, 4), (8, 1, 2), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_squeeze_0[grid(256)](buf2, primals_7, buf3,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
        del primals_7
    return buf3, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf0, (4, 2, 4), (8, 1, 2), 0
        ), reinterpret_tensor(buf1, (4, 4, 2), (8, 2, 1), 0)


class GroundingNew(nn.Module):

    def __init__(self, cfgT, cfgI, heads=1):
        super(GroundingNew, self).__init__()
        projection = cfgI.hidden_size // 2
        self.num_attention_heads = heads
        self.attention_head_size = int(projection // self.num_attention_heads)
        self.all_head_size = (self.num_attention_heads * self.
            attention_head_size)
        self.Q = nn.Linear(cfgT.hidden_size, self.all_head_size)
        self.K = nn.Linear(cfgI.hidden_size, self.all_head_size)
        self.cfgT = cfgT
        self.cfgI = cfgI

    def transpose(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.
            attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.Q.weight
        primals_2 = self.Q.bias
        primals_4 = self.K.weight
        primals_5 = self.K.bias
        primals_3 = input_0
        primals_6 = input_1
        primals_7 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
