# AOT ID: ['25_inference']
from ctypes import c_void_p, c_long
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

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
alloc_from_pool = torch.ops.inductor._alloc_from_pool
reinterpret_tensor = torch.ops.inductor._reinterpret_tensor
async_compile = AsyncCompile()


# kernel path: /tmp/torchinductor_root/jd/cjdpzu3av6gm2wmibwuibcbne3ru3vklay6af3rnj5vetzak545s.py
# Source Nodes: [q, q_1], Original ATen: [aten.div, aten.view]
# q => div
# q_1 => view
triton_poi_fused_div_view_0 = async_compile.triton('triton_', '''
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=[8388608], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp16', 1: '*fp16', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=89, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=1536, multi_processor_count=58), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_view_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': '72c34bdb145549777ca2f0838f26abe42bb446cf528c78d229508b5a55e67a78', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': False, '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_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask).to(tl.float32)
    tmp1 = 0.0625
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')

import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream


# kernel path: /tmp/torchinductor_root/6k/c6kak524kqjjktjalgl2uffcowjftev6b7vsojlzd7rgtya4sjoh.py
# Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution]
# relative_emb => convert_element_type, convert_element_type_1, convolution
triton_poi_fused__to_copy_convolution_1 = async_compile.triton('triton_', '''
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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp16', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=89, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=1536, multi_processor_count=58), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_convolution_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': '72c34bdb145549777ca2f0838f26abe42bb446cf528c78d229508b5a55e67a78', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': False, '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_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 57600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: /tmp/torchinductor_root/k7/ck7y55saw3jkzi723vcjab7klidpesejm2phmkdilbijhaiqycno.py
# Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution]
# relative_emb => convert_element_type, convert_element_type_1, convolution
triton_poi_fused__to_copy_convolution_2 = async_compile.triton('triton_', '''
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: '*fp16', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=89, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=1536, multi_processor_count=58), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_convolution_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': '72c34bdb145549777ca2f0838f26abe42bb446cf528c78d229508b5a55e67a78', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': False, '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_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 225
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: /tmp/torchinductor_root/qg/cqg2xaqobc45rohm5gdrm4as3gmj7rygpiuwz5ymxu4bvsvb225o.py
# Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution]
# relative_emb => convert_element_type, convert_element_type_1, convolution
triton_poi_fused__to_copy_convolution_3 = async_compile.triton('triton_', '''
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=[4194304], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp16', 1: '*fp16', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=89, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=1536, multi_processor_count=58), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': '72c34bdb145549777ca2f0838f26abe42bb446cf528c78d229508b5a55e67a78', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': False, '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_(in_out_ptr0, in_ptr0, ks0, xnumel, XBLOCK : tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // ks0)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1 = args
    args.clear()
    s0 = arg0_1
    s1 = arg1_1
    assert_size_stride(arg2_1, (1, 256, s0, s1), (256*s0*s1, s0*s1, s1, 1))
    assert_size_stride(arg3_1, (225, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(arg4_1, (225, ), (1, ))
    assert_size_stride(arg5_1, (1, 256, s0, s1), (256*s0*s1, s0*s1, s1, 1))
    assert_size_stride(arg6_1, (1, 256, s0, s1), (256*s0*s1, s0*s1, s1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 256, s0, s1), (256*s0*s1, s0*s1, s1, 1), torch.float16)
        # Source Nodes: [q, q_1], Original ATen: [aten.div, aten.view]
        triton_poi_fused_div_view_0_xnumel = 256*s0*s1
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_view_0.run(arg5_1, buf0, triton_poi_fused_div_view_0_xnumel, grid=grid(triton_poi_fused_div_view_0_xnumel), stream=stream0)
        buf1 = empty_strided_cuda((225, 256, 1, 1), (256, 1, 1, 1), torch.float16)
        # Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution]
        triton_poi_fused__to_copy_convolution_1.run(arg3_1, buf1, 57600, grid=grid(57600), stream=stream0)
        del arg3_1
        buf2 = empty_strided_cuda((225, ), (1, ), torch.float16)
        # Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution]
        triton_poi_fused__to_copy_convolution_2.run(arg4_1, buf2, 225, grid=grid(225), stream=stream0)
        del arg4_1
        # Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution]
        buf3 = extern_kernels.convolution(arg5_1, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (1, 225, s0, s1), (225*s0*s1, s0*s1, s1, 1))
        del arg5_1
        del buf1
        ps0 = s0*s1
        buf4 = buf3; del buf3  # reuse
        # Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution]
        triton_poi_fused__to_copy_convolution_3_xnumel = 225*s0*s1
        triton_poi_fused__to_copy_convolution_3.run(buf4, buf2, ps0, triton_poi_fused__to_copy_convolution_3_xnumel, grid=grid(triton_poi_fused__to_copy_convolution_3_xnumel), stream=stream0)
        del buf2
    return (buf0, arg6_1, reinterpret_tensor(arg2_1, (1, 1, 256, s0*s1), (256*s0*s1, 256*s0*s1, s0*s1, 1), 0), s0, s1, reinterpret_tensor(buf4, (1, 1, 225, s0*s1), (225*s0*s1, 225*s0*s1, s0*s1, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = 136
    arg1_1 = 136
    arg2_1 = rand_strided((1, 256, 136, 136), (4734976, 18496, 136, 1), device='cuda:0', dtype=torch.float16)
    arg3_1 = rand_strided((225, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    arg4_1 = rand_strided((225, ), (1, ), device='cuda:0', dtype=torch.float32)
    arg5_1 = rand_strided((1, 256, 136, 136), (4734976, 18496, 136, 1), device='cuda:0', dtype=torch.float16)
    arg6_1 = rand_strided((1, 256, 136, 136), (4734976, 18496, 136, 1), device='cuda:0', dtype=torch.float16)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1])
    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)