# 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/qy/cqygb2qwws4alrj2ckver6xxou33xclrdrez4nmivbvxxa23xdtf.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[1024, 4096], 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', '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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1024
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), None, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (1048576*y1)), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/u7/cu7a662azidbq3tmhjleikux7irx7jvowvfwsbmc4iwezhqjgc3c.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_4, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[128, 4096], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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_convolution_1', '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_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 128
    xnumel = 4096
    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 = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 32
    y1 = (yindex // 32)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (32*x2) + (131072*y1)), ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4096*y3)), tmp2, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/gw/cgw47fm7lqkgooogmtmaw2mqt4hnfuh7negcehsiawlcdu4iaphj.py
# Topologically Sorted Source Nodes: [mask_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   mask_1 => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_red_fused__softmax_2 = async_compile.triton('triton_red_fused__softmax_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.reduction(
    size_hints=[16384, 4096],
    reduction_hint=ReductionHint.INNER,
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, '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}
)
@triton.jit
def triton_red_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 16384
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp2 = tl.full([XBLOCK, RBLOCK], float("-inf"), tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + (4096*x0)), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp3 = triton_helpers.maximum(_tmp2, tmp1)
        _tmp2 = tl.where(rmask, tmp3, _tmp2)
    tmp2 = triton_helpers.max2(_tmp2, 1)[:, None]
    _tmp8 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp4 = tl.load(in_ptr0 + (r1 + (4096*x0)), rmask, eviction_policy='evict_last', other=0.0)
        tmp5 = tmp4 - tmp2
        tmp6 = tl_math.exp(tmp5)
        tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
        tmp9 = _tmp8 + tmp7
        _tmp8 = tl.where(rmask, tmp9, _tmp8)
    tmp8 = tl.sum(_tmp8, 1)[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + (4096*x0)), rmask, eviction_policy='evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tl_math.exp(tmp11)
        tmp13 = tmp12 / tmp8
        tl.store(out_ptr2 + (r1 + (4096*x0)), tmp13, rmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/uc/cucqjmgbyhlerewb4vgxo6itmnpcw4qkazlfq7ordh6mg42vaq3y.py
# Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_7, %primals_8, [1, 1], [0, 0], [1, 1], False, [0, 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=[1024, 4096], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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_convolution_3', '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_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1024
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (256*x2) + (1048576*y1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4096*y3)), tmp2, None)
''', 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, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_3, (32, ), (1, ))
    assert_size_stride(primals_4, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_5, (32, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_6, (32, ), (1, ))
    assert_size_stride(primals_7, (256, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_8, (256, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 1024, 4096, grid=grid(1024, 4096), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_0.run(primals_4, buf1, 1024, 4096, grid=grid(1024, 4096), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 64, 64), (131072, 1, 2048, 32))
        buf3 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf2, primals_3, buf3, 128, 4096, grid=grid(128, 4096), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf1, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 32, 64, 64), (131072, 1, 2048, 32))
        buf5 = reinterpret_tensor(buf2, (4, 32, 64, 64), (131072, 4096, 64, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf4, primals_6, buf5, 128, 4096, grid=grid(128, 4096), stream=stream0)
        del buf4
        del primals_6
        buf6 = empty_strided_cuda((4, 4096, 4096), (16777216, 4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mask], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 4096, 32), (131072, 1, 4096), 0), reinterpret_tensor(buf5, (4, 32, 4096), (131072, 4096, 1), 0), out=buf6)
        buf9 = empty_strided_cuda((4, 4096, 4096), (16777216, 4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mask_1], Original ATen: [aten._softmax]
        triton_red_fused__softmax_2.run(buf6, buf9, 16384, 4096, grid=grid(16384), stream=stream0)
        del buf6
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf0, primals_7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf11 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf10, primals_8, buf11, 1024, 4096, grid=grid(1024, 4096), stream=stream0)
        del primals_8
        buf12 = reinterpret_tensor(buf10, (4, 256, 4096), (1048576, 4096, 1), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [feat], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf11, (4, 256, 4096), (1048576, 4096, 1), 0), reinterpret_tensor(buf9, (4, 4096, 4096), (16777216, 1, 4096), 0), out=buf12)
    return (reinterpret_tensor(buf12, (4, 256, 64, 64), (1048576, 4096, 64, 1), 0), buf0, primals_2, buf1, primals_5, primals_7, buf9, reinterpret_tensor(buf11, (4, 4096, 256), (1048576, 1, 4096), 0), reinterpret_tensor(buf3, (4, 32, 4096), (131072, 4096, 1), 0), reinterpret_tensor(buf5, (4, 4096, 32), (131072, 1, 4096), 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, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((256, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn


class CrossAttention(nn.Module):

    def __init__(self, in_channel=256, ratio=8):
        super(CrossAttention, self).__init__()
        self.conv_query = nn.Conv2d(in_channel, in_channel // ratio,
            kernel_size=1)
        self.conv_key = nn.Conv2d(in_channel, in_channel // ratio,
            kernel_size=1)
        self.conv_value = nn.Conv2d(in_channel, in_channel, kernel_size=1)

    def forward(self, rgb, depth):
        bz, c, h, w = rgb.shape
        depth_q = self.conv_query(depth).view(bz, -1, h * w).permute(0, 2, 1)
        depth_k = self.conv_key(depth).view(bz, -1, h * w)
        mask = torch.bmm(depth_q, depth_k)
        mask = torch.softmax(mask, dim=-1)
        rgb_v = self.conv_value(rgb).view(bz, c, -1)
        feat = torch.bmm(rgb_v, mask.permute(0, 2, 1))
        feat = feat.view(bz, c, h, w)
        return feat


def get_inputs():
    return [torch.rand([4, 256, 64, 64]), torch.rand([4, 256, 64, 64])]


def get_init_inputs():
    return [[], {}]

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
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 256 * x2 + 1048576 * y1), tmp0, None)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 128
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 32
    y1 = yindex // 32
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 32 * x2 + 131072 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


@triton.jit
def triton_red_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp2 = tl.full([XBLOCK, RBLOCK], float('-inf'), tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp3 = triton_helpers.maximum(_tmp2, tmp1)
        _tmp2 = tl.where(rmask, tmp3, _tmp2)
    tmp2 = triton_helpers.max2(_tmp2, 1)[:, None]
    _tmp8 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp4 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp5 = tmp4 - tmp2
        tmp6 = tl_math.exp(tmp5)
        tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
        tmp9 = _tmp8 + tmp7
        _tmp8 = tl.where(rmask, tmp9, _tmp8)
    tmp8 = tl.sum(_tmp8, 1)[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tl_math.exp(tmp11)
        tmp13 = tmp12 / tmp8
        tl.store(out_ptr2 + (r1 + 4096 * x0), tmp13, rmask)


@triton.jit
def triton_poi_fused_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 256
    y1 = yindex // 256
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 256 * x2 + 1048576 * y1), None,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, None)


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, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_5, (32, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_6, (32,), (1,))
    assert_size_stride(primals_7, (256, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_8, (256,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(1024, 4096)](primals_1, buf0, 1024, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        triton_poi_fused_0[grid(1024, 4096)](primals_4, buf1, 1024, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 64, 64), (131072, 1, 2048, 32))
        buf3 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_1[grid(128, 4096)](buf2, primals_3,
            buf3, 128, 4096, XBLOCK=64, YBLOCK=64, num_warps=8, num_stages=1)
        del primals_3
        buf4 = extern_kernels.convolution(buf1, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 32, 64, 64), (131072, 1, 2048, 32))
        buf5 = reinterpret_tensor(buf2, (4, 32, 64, 64), (131072, 4096, 64,
            1), 0)
        del buf2
        triton_poi_fused_convolution_1[grid(128, 4096)](buf4, primals_6,
            buf5, 128, 4096, XBLOCK=64, YBLOCK=64, num_warps=8, num_stages=1)
        del buf4
        del primals_6
        buf6 = empty_strided_cuda((4, 4096, 4096), (16777216, 4096, 1),
            torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 4096, 32), (131072,
            1, 4096), 0), reinterpret_tensor(buf5, (4, 32, 4096), (131072, 
            4096, 1), 0), out=buf6)
        buf9 = empty_strided_cuda((4, 4096, 4096), (16777216, 4096, 1),
            torch.float32)
        triton_red_fused__softmax_2[grid(16384)](buf6, buf9, 16384, 4096,
            XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        del buf6
        buf10 = extern_kernels.convolution(buf0, primals_7, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf11 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_3[grid(1024, 4096)](buf10, primals_8,
            buf11, 1024, 4096, XBLOCK=16, YBLOCK=256, num_warps=8, num_stages=1
            )
        del primals_8
        buf12 = reinterpret_tensor(buf10, (4, 256, 4096), (1048576, 4096, 1), 0
            )
        del buf10
        extern_kernels.bmm(reinterpret_tensor(buf11, (4, 256, 4096), (
            1048576, 4096, 1), 0), reinterpret_tensor(buf9, (4, 4096, 4096),
            (16777216, 1, 4096), 0), out=buf12)
    return reinterpret_tensor(buf12, (4, 256, 64, 64), (1048576, 4096, 64, 
        1), 0
        ), buf0, primals_2, buf1, primals_5, primals_7, buf9, reinterpret_tensor(
        buf11, (4, 4096, 256), (1048576, 1, 4096), 0), reinterpret_tensor(buf3,
        (4, 32, 4096), (131072, 4096, 1), 0), reinterpret_tensor(buf5, (4, 
        4096, 32), (131072, 1, 4096), 0)


class CrossAttentionNew(nn.Module):

    def __init__(self, in_channel=256, ratio=8):
        super(CrossAttentionNew, self).__init__()
        self.conv_query = nn.Conv2d(in_channel, in_channel // ratio,
            kernel_size=1)
        self.conv_key = nn.Conv2d(in_channel, in_channel // ratio,
            kernel_size=1)
        self.conv_value = nn.Conv2d(in_channel, in_channel, kernel_size=1)

    def forward(self, input_0, input_1):
        primals_2 = self.conv_query.weight
        primals_3 = self.conv_query.bias
        primals_5 = self.conv_key.weight
        primals_6 = self.conv_key.bias
        primals_7 = self.conv_value.weight
        primals_8 = self.conv_value.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
