# 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/xy/cxy77nk6tbfgwqyn5amat6tjjfsjhqlutzehwqrmr3xcuh7sc46s.py
# Topologically Sorted Source Nodes: [v_out_1, k_out_2, einsum], Original ATen: [aten.unfold, aten.cat, aten.mul]
# Source node to ATen node mapping:
#   einsum => mul_1
#   k_out_2 => cat
#   v_out_1 => unfold_3
# Graph fragment:
#   %unfold_3 : [num_users=2] = call_function[target=torch.ops.aten.unfold.default](args = (%unfold_2, 3, 4, 1), kwargs = {})
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%add, %add_1], 1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute, %permute_1), kwargs = {})
triton_poi_fused_cat_mul_unfold_0 = async_compile.triton('triton_poi_fused_cat_mul_unfold_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: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_mul_unfold_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_mul_unfold_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x3 = (xindex // 16) % 4
    x4 = (xindex // 64)
    x5 = xindex % 16
    x2 = (xindex // 4) % 4
    x1 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp20 = tl.load(in_ptr3 + (x0), xmask)
    tmp1 = x3
    tmp2 = tl.full([1], 0, tl.int64)
    tmp3 = tmp1 >= tmp2
    tmp4 = tl.full([1], 2, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = tl.load(in_ptr0 + (x5 + (16*x3) + (64*x4)), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (x2 + (4*x3)), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tmp11 = tmp1 >= tmp4
    tmp12 = tl.full([1], 4, tl.int64)
    tmp13 = tmp1 < tmp12
    tmp14 = tl.load(in_ptr0 + (32 + x5 + (16*((-2) + x3)) + (64*x4)), tmp11 & xmask, other=0.0)
    tmp15 = tl.load(in_ptr2 + (x1 + (4*((-2) + x3))), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp11, tmp16, tmp17)
    tmp19 = tl.where(tmp5, tmp10, tmp18)
    tmp21 = 0.0
    tmp22 = tmp19 >= tmp21
    tmp23 = 1.0
    tmp24 = -1.0
    tmp25 = tl.where(tmp22, tmp23, tmp24)
    tmp26 = tmp20 * tmp25
    tmp27 = tmp26 - tmp26
    tmp28 = tmp25 * tmp19
    tmp29 = tmp27 * tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp30 / tmp30
    tmp32 = tmp31 * tmp0
    tl.store(in_out_ptr0 + (x0), tmp0, xmask)
    tl.store(out_ptr0 + (x0), tmp19, xmask)
    tl.store(out_ptr1 + (x0), tmp32, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (2, 1, 1, 4, 1), (4, 4, 4, 1, 1))
    assert_size_stride(primals_6, (2, 1, 1, 1, 4), (4, 4, 4, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [q_out], 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, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [k_out], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_1, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [v_out], Original ATen: [aten.convolution]
        buf2 = 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(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = reinterpret_tensor(buf2, (4, 4, 1, 1, 4, 4), (64, 16, 16, 4, 4, 1), 0); del buf2  # reuse
        buf4 = empty_strided_cuda((4, 4, 1, 1, 4, 4), (64, 16, 16, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4, 4, 4, 1), (64, 64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v_out_1, k_out_2, einsum], Original ATen: [aten.unfold, aten.cat, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_mul_unfold_0.run(buf3, buf1, primals_5, primals_6, buf0, buf4, buf5, 256, grid=grid(256), stream=stream0)
        del buf1
        del primals_5
        del primals_6
    return (reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_1, primals_2, primals_3, primals_4, buf0, buf3, buf4, )


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((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, 1, 1, 4, 1), (4, 4, 4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((2, 1, 1, 1, 4), (4, 4, 4, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init


class AttentionConv(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, groups=1, bias=False):
        super(AttentionConv, self).__init__()
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.groups = groups
        assert self.out_channels % self.groups == 0, 'out_channels should be divided by groups. (example: out_channels: 40, groups: 4)'
        self.rel_h = nn.Parameter(torch.randn(out_channels // 2, 1, 1,
            kernel_size, 1), requires_grad=True)
        self.rel_w = nn.Parameter(torch.randn(out_channels // 2, 1, 1, 1,
            kernel_size), requires_grad=True)
        self.key_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1,
            bias=bias)
        self.query_conv = nn.Conv2d(in_channels, out_channels, kernel_size=
            1, bias=bias)
        self.value_conv = nn.Conv2d(in_channels, out_channels, kernel_size=
            1, bias=bias)
        self.reset_parameters()

    def forward(self, x):
        batch, _channels, height, width = x.size()
        padded_x = F.pad(x, [self.padding, self.padding, self.padding, self
            .padding])
        q_out = self.query_conv(x)
        k_out = self.key_conv(padded_x)
        v_out = self.value_conv(padded_x)
        k_out = k_out.unfold(2, self.kernel_size, self.stride).unfold(3,
            self.kernel_size, self.stride)
        v_out = v_out.unfold(2, self.kernel_size, self.stride).unfold(3,
            self.kernel_size, self.stride)
        k_out_h, k_out_w = k_out.split(self.out_channels // 2, dim=1)
        k_out = torch.cat((k_out_h + self.rel_h, k_out_w + self.rel_w), dim=1)
        k_out = k_out.contiguous().view(batch, self.groups, self.
            out_channels // self.groups, height, width, -1)
        v_out = v_out.contiguous().view(batch, self.groups, self.
            out_channels // self.groups, height, width, -1)
        q_out = q_out.view(batch, self.groups, self.out_channels // self.
            groups, height, width, 1)
        out = q_out * k_out
        out = F.softmax(out, dim=-1)
        out = torch.einsum('bnchwk,bnchwk -> bnchw', out, v_out).view(batch,
            -1, height, width)
        return out

    def reset_parameters(self):
        init.kaiming_normal_(self.key_conv.weight, mode='fan_out',
            nonlinearity='relu')
        init.kaiming_normal_(self.value_conv.weight, mode='fan_out',
            nonlinearity='relu')
        init.kaiming_normal_(self.query_conv.weight, mode='fan_out',
            nonlinearity='relu')
        init.normal_(self.rel_h, 0, 1)
        init.normal_(self.rel_w, 0, 1)


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.init as init
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_cat_mul_unfold_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x3 = xindex // 16 % 4
    x4 = xindex // 64
    x5 = xindex % 16
    x2 = xindex // 4 % 4
    x1 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp20 = tl.load(in_ptr3 + x0, xmask)
    tmp1 = x3
    tl.full([1], 0, tl.int64)
    tmp4 = tl.full([1], 2, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = tl.load(in_ptr0 + (x5 + 16 * x3 + 64 * x4), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (x2 + 4 * x3), tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tmp11 = tmp1 >= tmp4
    tl.full([1], 4, tl.int64)
    tmp14 = tl.load(in_ptr0 + (32 + x5 + 16 * (-2 + x3) + 64 * x4), tmp11 &
        xmask, other=0.0)
    tmp15 = tl.load(in_ptr2 + (x1 + 4 * (-2 + x3)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp11, tmp16, tmp17)
    tmp19 = tl.where(tmp5, tmp10, tmp18)
    tmp21 = 0.0
    tmp22 = tmp19 >= tmp21
    tmp23 = 1.0
    tmp24 = -1.0
    tmp25 = tl.where(tmp22, tmp23, tmp24)
    tmp26 = tmp20 * tmp25
    tmp27 = tmp26 - tmp26
    tmp28 = tmp25 * tmp19
    tmp29 = tmp27 * tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp30 / tmp30
    tmp32 = tmp31 * tmp0
    tl.store(in_out_ptr0 + x0, tmp0, xmask)
    tl.store(out_ptr0 + x0, tmp19, xmask)
    tl.store(out_ptr1 + x0, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (2, 1, 1, 4, 1), (4, 4, 4, 1, 1))
    assert_size_stride(primals_6, (2, 1, 1, 1, 4), (4, 4, 4, 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, 4, 4, 4), (64, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_1, primals_3, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = 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(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = reinterpret_tensor(buf2, (4, 4, 1, 1, 4, 4), (64, 16, 16, 4,
            4, 1), 0)
        del buf2
        buf4 = empty_strided_cuda((4, 4, 1, 1, 4, 4), (64, 16, 16, 16, 4, 1
            ), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4, 4, 4, 1), (64, 64, 16, 4, 1, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_mul_unfold_0[grid(256)](buf3, buf1, primals_5,
            primals_6, buf0, buf4, buf5, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf1
        del primals_5
        del primals_6
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, primals_2, primals_3, primals_4, buf0, buf3, buf4


class AttentionConvNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, groups=1, bias=False):
        super(AttentionConvNew, self).__init__()
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.groups = groups
        assert self.out_channels % self.groups == 0, 'out_channels should be divided by groups. (example: out_channels: 40, groups: 4)'
        self.rel_h = nn.Parameter(torch.randn(out_channels // 2, 1, 1,
            kernel_size, 1), requires_grad=True)
        self.rel_w = nn.Parameter(torch.randn(out_channels // 2, 1, 1, 1,
            kernel_size), requires_grad=True)
        self.key_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1,
            bias=bias)
        self.query_conv = nn.Conv2d(in_channels, out_channels, kernel_size=
            1, bias=bias)
        self.value_conv = nn.Conv2d(in_channels, out_channels, kernel_size=
            1, bias=bias)
        self.reset_parameters()

    def reset_parameters(self):
        init.kaiming_normal_(self.key_conv.weight, mode='fan_out',
            nonlinearity='relu')
        init.kaiming_normal_(self.value_conv.weight, mode='fan_out',
            nonlinearity='relu')
        init.kaiming_normal_(self.query_conv.weight, mode='fan_out',
            nonlinearity='relu')
        init.normal_(self.rel_h, 0, 1)
        init.normal_(self.rel_w, 0, 1)

    def forward(self, input_0):
        primals_5 = self.rel_h
        primals_6 = self.rel_w
        primals_2 = self.key_conv.weight
        primals_3 = self.query_conv.weight
        primals_4 = self.value_conv.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
