# 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_7/inductor_cache/sr/csrg6irduolxnaubd5v3tlh5eeuhw27sxkg3o56t4veh47sq6ce3.py
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[128], 
    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_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 2
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nc/cncnuxsvfk7dt4mkzeodrk2ndisdyvum53becalvxeuaxtixinwt.py
# Topologically Sorted Source Nodes: [pairwise_weight_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   pairwise_weight_2 => div_1, exp, sum_1
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1.4142135623730951), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_1 = async_compile.triton('triton_per_fused__softmax_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.persistent_reduction(
    size_hints=[64, 16],
    reduction_hint=ReductionHint.INNER,
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float("-inf"))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp7 = tmp2 - tmp6
    tmp8 = 0.7071067811865475
    tmp9 = tmp7 * tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jf/cjfexrwvsh2fcsd3gxdl7cjeqawt22nkkqfrddz4juscdmtol362.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_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=[8, 16], 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', '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_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 2
    y1 = (yindex // 2)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (2*x2) + (32*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4g/c4ggrwz5dwisbmsoqxjaytjtns6mjlaxldj2bvi5bb2qmv2mmmle.py
# Topologically Sorted Source Nodes: [conv2d_3, output], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
#   output => add
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_12, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %convolution_3), kwargs = {})
triton_poi_fused_add_convolution_3 = async_compile.triton('triton_poi_fused_add_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=[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_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_convolution_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + (x3), 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, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (2, ), (1, ))
    assert_size_stride(primals_4, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (2, ), (1, ))
    assert_size_stride(primals_6, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (2, ), (1, ))
    assert_size_stride(primals_8, (4, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 4, 4), (32, 16, 4, 1))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_1, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 4, 4), (32, 16, 4, 1))
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_1, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 2, 4, 4), (32, 16, 4, 1))
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf3, primals_5, 128, grid=grid(128), stream=stream0)
        del primals_5
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf4, primals_7, 128, grid=grid(128), stream=stream0)
        del primals_7
        buf5 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pairwise_weight], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 16, 2), (32, 1, 16), 0), reinterpret_tensor(buf4, (4, 2, 16), (32, 16, 1), 0), out=buf5)
        buf8 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pairwise_weight_2], Original ATen: [aten._softmax]
        triton_per_fused__softmax_1.run(buf5, buf8, 64, 16, grid=grid(64), stream=stream0)
        del buf5
        buf9 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf9, primals_3, 128, grid=grid(128), stream=stream0)
        del primals_3
        buf10 = empty_strided_cuda((4, 16, 2), (32, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf8, reinterpret_tensor(buf9, (4, 16, 2), (32, 1, 16), 0), out=buf10)
        buf11 = empty_strided_cuda((4, 2, 16), (32, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf10, buf11, 8, 16, grid=grid(8, 16), stream=stream0)
        del buf10
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(reinterpret_tensor(buf11, (4, 2, 4, 4), (32, 16, 4, 1), 0), primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, output], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_3.run(buf13, primals_1, primals_9, 256, grid=grid(256), stream=stream0)
        del primals_9
    return (reinterpret_tensor(buf13, (16, 4, 4), (1, 64, 16), 0), primals_1, primals_2, primals_4, primals_6, primals_8, buf8, reinterpret_tensor(buf11, (4, 2, 4, 4), (32, 16, 4, 1), 0), reinterpret_tensor(buf9, (4, 2, 16), (32, 16, 1), 0), reinterpret_tensor(buf3, (4, 2, 16), (32, 16, 1), 0), reinterpret_tensor(buf4, (4, 16, 2), (32, 1, 16), 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((2, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((2, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 2, 1, 1), (2, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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
from torch import Tensor
from torch import nn
import torch.utils.data
from typing import Optional
from abc import ABCMeta


class _NonLocalNd(nn.Module, metaclass=ABCMeta):
    """Basic Non-local module.

    This module is proposed in
    "Non-local Neural Networks"
    Paper reference: https://arxiv.org/abs/1711.07971
    Code reference: https://github.com/AlexHex7/Non-local_pytorch

    Args:
        in_channels (int): Channels of the input feature map.
        reduction (int): Channel reduction ratio. Default: 2.
        use_scale (bool): Whether to scale pairwise_weight by
            `1/sqrt(inter_channels)` when the mode is `embedded_gaussian`.
            Default: True.
        conv_cfg (None | dict): The config dict for convolution layers.
            If not specified, it will use `nn.Conv2d` for convolution layers.
            Default: None.
        norm_cfg (None | dict): The config dict for normalization layers.
            Default: None. (This parameter is only applicable to conv_out.)
        mode (str): Options are `gaussian`, `concatenation`,
            `embedded_gaussian` and `dot_product`. Default: embedded_gaussian.
    """

    def __init__(self, in_channels, reduction=2, use_scale=True, conv_cfg=
        None, norm_cfg=None, mode='embedded_gaussian', **kwargs):
        super(_NonLocalNd, self).__init__()
        self.in_channels = in_channels
        self.reduction = reduction
        self.use_scale = use_scale
        self.inter_channels = max(in_channels // reduction, 1)
        self.mode = mode
        if mode not in ['gaussian', 'embedded_gaussian', 'dot_product',
            'concatenation']:
            raise ValueError(
                f"Mode should be in 'gaussian', 'concatenation', 'embedded_gaussian' or 'dot_product', but got {mode} instead."
                )
        self.g = nn.Conv2d(self.in_channels, self.inter_channels, 1)
        self.conv_out = nn.Conv2d(self.inter_channels, self.in_channels, 1)
        if self.mode != 'gaussian':
            self.theta = nn.Conv2d(self.in_channels, self.inter_channels, 1)
            self.phi = nn.Conv2d(self.in_channels, self.inter_channels, 1)
        if self.mode == 'concatenation':
            self.concat_project = nn.Sequential(nn.Conv2d(self.
                inter_channels * 2, 1, kernel_size=1, stride=1, padding=0,
                bias=False), nn.ReLU())

    def gaussian(self, theta_x, phi_x):
        pairwise_weight = torch.matmul(theta_x, phi_x)
        pairwise_weight = pairwise_weight.softmax(dim=-1)
        return pairwise_weight

    def embedded_gaussian(self, theta_x, phi_x):
        pairwise_weight = torch.matmul(theta_x, phi_x)
        if self.use_scale:
            pairwise_weight /= theta_x.shape[-1] ** 0.5
        pairwise_weight = pairwise_weight.softmax(dim=-1)
        return pairwise_weight

    def dot_product(self, theta_x, phi_x):
        pairwise_weight = torch.matmul(theta_x, phi_x)
        pairwise_weight /= pairwise_weight.shape[-1]
        return pairwise_weight

    def concatenation(self, theta_x, phi_x):
        h = theta_x.size(2)
        w = phi_x.size(3)
        theta_x = theta_x.repeat(1, 1, 1, w)
        phi_x = phi_x.repeat(1, 1, h, 1)
        concat_feature = torch.cat([theta_x, phi_x], dim=1)
        pairwise_weight = self.concat_project(concat_feature)
        n, _, h, w = pairwise_weight.size()
        pairwise_weight = pairwise_weight.view(n, h, w)
        pairwise_weight /= pairwise_weight.shape[-1]
        return pairwise_weight

    def forward(self, x):
        n = x.size(0)
        g_x = self.g(x).view(n, self.inter_channels, -1)
        g_x = g_x.permute(0, 2, 1)
        if self.mode == 'gaussian':
            theta_x = x.view(n, self.in_channels, -1)
            theta_x = theta_x.permute(0, 2, 1)
            if self.sub_sample:
                phi_x = self.phi(x).view(n, self.in_channels, -1)
            else:
                phi_x = x.view(n, self.in_channels, -1)
        elif self.mode == 'concatenation':
            theta_x = self.theta(x).view(n, self.inter_channels, -1, 1)
            phi_x = self.phi(x).view(n, self.inter_channels, 1, -1)
        else:
            theta_x = self.theta(x).view(n, self.inter_channels, -1)
            theta_x = theta_x.permute(0, 2, 1)
            phi_x = self.phi(x).view(n, self.inter_channels, -1)
        pairwise_func = getattr(self, self.mode)
        pairwise_weight = pairwise_func(theta_x, phi_x)
        y = torch.matmul(pairwise_weight, g_x)
        y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels,
            *x.size()[2:])
        output = x + self.conv_out(y)
        return output


class NonLocal2d(_NonLocalNd):
    """2D Non-local module.

    Args:
        in_channels (int): Same as `NonLocalND`.
        sub_sample (bool): Whether to apply max pooling after pairwise
            function (Note that the `sub_sample` is applied on spatial only).
            Default: False.
        conv_cfg (None | dict): Same as `NonLocalND`.
            Default: dict(type='Conv2d').
    """
    _abbr_ = 'nonlocal_block'

    def __init__(self, in_channels, sub_sample=False, conv_cfg=dict(type=
        'Conv2d'), **kwargs):
        super(NonLocal2d, self).__init__(in_channels, conv_cfg=conv_cfg, **
            kwargs)
        self.sub_sample = sub_sample
        if sub_sample:
            max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
            self.g = nn.Sequential(self.g, max_pool_layer)
            if self.mode != 'gaussian':
                self.phi = nn.Sequential(self.phi, max_pool_layer)
            else:
                self.phi = max_pool_layer


class NonLocalEncoder(nn.Module):

    def __init__(self, d_model, norm=None):
        super().__init__()
        self.layer = NonLocal2d(d_model)
        self.norm = norm

    def forward(self, src, mask: 'Optional[Tensor]'=None,
        src_key_padding_mask: 'Optional[Tensor]'=None, pos:
        'Optional[Tensor]'=None):
        output = src
        output = self.layer(src)
        output = output.flatten(2).permute(2, 0, 1)
        if self.norm is not None:
            output = self.norm(output)
        return output


def get_inputs():
    return [torch.rand([4, 4, 4, 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
from torch import nn
import torch.utils.data
from abc import ABCMeta
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 2
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float('-inf'))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp7 = tmp2 - tmp6
    tmp8 = 0.7071067811865475
    tmp9 = tmp7 * tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp15, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 8
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 2
    y1 = yindex // 2
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 2 * x2 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_3(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (2,), (1,))
    assert_size_stride(primals_6, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (2,), (1,))
    assert_size_stride(primals_8, (4, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 4, 4), (32, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_1, primals_4, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 4, 4), (32, 16, 4, 1))
        buf2 = extern_kernels.convolution(primals_1, primals_6, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 2, 4, 4), (32, 16, 4, 1))
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(128)](buf3, primals_5, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = buf2
        del buf2
        triton_poi_fused_convolution_0[grid(128)](buf4, primals_7, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf5 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 16, 2), (32, 1, 16),
            0), reinterpret_tensor(buf4, (4, 2, 16), (32, 16, 1), 0), out=buf5)
        buf8 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(64)](buf5, buf8, 64, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del buf5
        buf9 = buf0
        del buf0
        triton_poi_fused_convolution_0[grid(128)](buf9, primals_3, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf10 = empty_strided_cuda((4, 16, 2), (32, 2, 1), torch.float32)
        extern_kernels.bmm(buf8, reinterpret_tensor(buf9, (4, 16, 2), (32, 
            1, 16), 0), out=buf10)
        buf11 = empty_strided_cuda((4, 2, 16), (32, 16, 1), torch.float32)
        triton_poi_fused_clone_2[grid(8, 16)](buf10, buf11, 8, 16, XBLOCK=
            16, YBLOCK=8, num_warps=4, num_stages=1)
        del buf10
        buf12 = extern_kernels.convolution(reinterpret_tensor(buf11, (4, 2,
            4, 4), (32, 16, 4, 1), 0), primals_8, stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_add_convolution_3[grid(256)](buf13, primals_1,
            primals_9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
    return (reinterpret_tensor(buf13, (16, 4, 4), (1, 64, 16), 0),
        primals_1, primals_2, primals_4, primals_6, primals_8, buf8,
        reinterpret_tensor(buf11, (4, 2, 4, 4), (32, 16, 4, 1), 0),
        reinterpret_tensor(buf9, (4, 2, 16), (32, 16, 1), 0),
        reinterpret_tensor(buf3, (4, 2, 16), (32, 16, 1), 0),
        reinterpret_tensor(buf4, (4, 16, 2), (32, 1, 16), 0))


class _NonLocalNd(nn.Module, metaclass=ABCMeta):
    """Basic Non-local module.

    This module is proposed in
    "Non-local Neural Networks"
    Paper reference: https://arxiv.org/abs/1711.07971
    Code reference: https://github.com/AlexHex7/Non-local_pytorch

    Args:
        in_channels (int): Channels of the input feature map.
        reduction (int): Channel reduction ratio. Default: 2.
        use_scale (bool): Whether to scale pairwise_weight by
            `1/sqrt(inter_channels)` when the mode is `embedded_gaussian`.
            Default: True.
        conv_cfg (None | dict): The config dict for convolution layers.
            If not specified, it will use `nn.Conv2d` for convolution layers.
            Default: None.
        norm_cfg (None | dict): The config dict for normalization layers.
            Default: None. (This parameter is only applicable to conv_out.)
        mode (str): Options are `gaussian`, `concatenation`,
            `embedded_gaussian` and `dot_product`. Default: embedded_gaussian.
    """

    def __init__(self, in_channels, reduction=2, use_scale=True, conv_cfg=
        None, norm_cfg=None, mode='embedded_gaussian', **kwargs):
        super(_NonLocalNd, self).__init__()
        self.in_channels = in_channels
        self.reduction = reduction
        self.use_scale = use_scale
        self.inter_channels = max(in_channels // reduction, 1)
        self.mode = mode
        if mode not in ['gaussian', 'embedded_gaussian', 'dot_product',
            'concatenation']:
            raise ValueError(
                f"Mode should be in 'gaussian', 'concatenation', 'embedded_gaussian' or 'dot_product', but got {mode} instead."
                )
        self.g = nn.Conv2d(self.in_channels, self.inter_channels, 1)
        self.conv_out = nn.Conv2d(self.inter_channels, self.in_channels, 1)
        if self.mode != 'gaussian':
            self.theta = nn.Conv2d(self.in_channels, self.inter_channels, 1)
            self.phi = nn.Conv2d(self.in_channels, self.inter_channels, 1)
        if self.mode == 'concatenation':
            self.concat_project = nn.Sequential(nn.Conv2d(self.
                inter_channels * 2, 1, kernel_size=1, stride=1, padding=0,
                bias=False), nn.ReLU())

    def gaussian(self, theta_x, phi_x):
        pairwise_weight = torch.matmul(theta_x, phi_x)
        pairwise_weight = pairwise_weight.softmax(dim=-1)
        return pairwise_weight

    def embedded_gaussian(self, theta_x, phi_x):
        pairwise_weight = torch.matmul(theta_x, phi_x)
        if self.use_scale:
            pairwise_weight /= theta_x.shape[-1] ** 0.5
        pairwise_weight = pairwise_weight.softmax(dim=-1)
        return pairwise_weight

    def dot_product(self, theta_x, phi_x):
        pairwise_weight = torch.matmul(theta_x, phi_x)
        pairwise_weight /= pairwise_weight.shape[-1]
        return pairwise_weight

    def concatenation(self, theta_x, phi_x):
        h = theta_x.size(2)
        w = phi_x.size(3)
        theta_x = theta_x.repeat(1, 1, 1, w)
        phi_x = phi_x.repeat(1, 1, h, 1)
        concat_feature = torch.cat([theta_x, phi_x], dim=1)
        pairwise_weight = self.concat_project(concat_feature)
        n, _, h, w = pairwise_weight.size()
        pairwise_weight = pairwise_weight.view(n, h, w)
        pairwise_weight /= pairwise_weight.shape[-1]
        return pairwise_weight

    def forward(self, x):
        n = x.size(0)
        g_x = self.g(x).view(n, self.inter_channels, -1)
        g_x = g_x.permute(0, 2, 1)
        if self.mode == 'gaussian':
            theta_x = x.view(n, self.in_channels, -1)
            theta_x = theta_x.permute(0, 2, 1)
            if self.sub_sample:
                phi_x = self.phi(x).view(n, self.in_channels, -1)
            else:
                phi_x = x.view(n, self.in_channels, -1)
        elif self.mode == 'concatenation':
            theta_x = self.theta(x).view(n, self.inter_channels, -1, 1)
            phi_x = self.phi(x).view(n, self.inter_channels, 1, -1)
        else:
            theta_x = self.theta(x).view(n, self.inter_channels, -1)
            theta_x = theta_x.permute(0, 2, 1)
            phi_x = self.phi(x).view(n, self.inter_channels, -1)
        pairwise_func = getattr(self, self.mode)
        pairwise_weight = pairwise_func(theta_x, phi_x)
        y = torch.matmul(pairwise_weight, g_x)
        y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels,
            *x.size()[2:])
        output = x + self.conv_out(y)
        return output


class NonLocal2d(_NonLocalNd):
    """2D Non-local module.

    Args:
        in_channels (int): Same as `NonLocalND`.
        sub_sample (bool): Whether to apply max pooling after pairwise
            function (Note that the `sub_sample` is applied on spatial only).
            Default: False.
        conv_cfg (None | dict): Same as `NonLocalND`.
            Default: dict(type='Conv2d').
    """
    _abbr_ = 'nonlocal_block'

    def __init__(self, in_channels, sub_sample=False, conv_cfg=dict(type=
        'Conv2d'), **kwargs):
        super(NonLocal2d, self).__init__(in_channels, conv_cfg=conv_cfg, **
            kwargs)
        self.sub_sample = sub_sample
        if sub_sample:
            max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
            self.g = nn.Sequential(self.g, max_pool_layer)
            if self.mode != 'gaussian':
                self.phi = nn.Sequential(self.phi, max_pool_layer)
            else:
                self.phi = max_pool_layer


class NonLocalEncoderNew(nn.Module):

    def __init__(self, d_model, norm=None):
        super().__init__()
        self.layer = NonLocal2d(d_model)
        self.norm = norm

    def forward(self, input_0):
        primals_2 = self.layer.g.weight
        primals_3 = self.layer.g.bias
        primals_8 = self.layer.conv_out.weight
        primals_9 = self.layer.conv_out.bias
        primals_4 = self.layer.theta.weight
        primals_5 = self.layer.theta.bias
        primals_6 = self.layer.phi.weight
        primals_7 = self.layer.phi.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
