# 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_6/inductor_cache/zo/czobpmlyr5atbcpsuque6vcmk7nafmb3smtbzoqilz46drm7zbkm.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_1, %primals_2, %primals_3, [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=[64], 
    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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/63/c63abizbceybowmxasyhbvyg6gmcs7m552tgg653wdhaqei77hz3.py
# Topologically Sorted Source Nodes: [energy_1, energy_sum, energy_2], Original ATen: [aten.exp, aten.sum, aten.div]
# Source node to ATen node mapping:
#   energy_1 => exp
#   energy_2 => div
#   energy_sum => sum_1
# Graph fragment:
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%bmm,), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused_div_exp_sum_1 = async_compile.triton('triton_per_fused_div_exp_sum_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: '*fp32', 3: 'i32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_exp_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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_div_exp_sum_1(in_ptr0, out_ptr0, out_ptr1, 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 = tl_math.exp(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = tmp1 / tmp5
    tl.store(out_ptr1 + (r1 + (16*x0)), tmp6, xmask)
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/w5/cw5gytijzzkwnfpq2a2axdsj4pfxgxmwiuzizuyd4bw5uwnanzw7.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_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_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=[256], 
    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_2', '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_2(in_out_ptr0, in_ptr0, 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_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')


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, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 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, 1, 4, 4), (16, 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, 1, 4, 4), (16, 16, 4, 1))
        buf2 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 1, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf2, primals_3, 64, grid=grid(64), stream=stream0)
        del primals_3
        buf3 = reinterpret_tensor(buf1, (4, 1, 4, 4), (16, 1, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf3, primals_5, 64, grid=grid(64), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [energy], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 16, 1), (16, 1, 0), 0), reinterpret_tensor(buf3, (4, 1, 16), (16, 0, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [energy_1, energy_sum, energy_2], Original ATen: [aten.exp, aten.sum, aten.div]
        triton_per_fused_div_exp_sum_1.run(buf4, buf5, buf6, 64, 16, grid=grid(64), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf7 = 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(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf8, primals_7, 256, grid=grid(256), stream=stream0)
        del primals_7
        buf9 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bmm_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf8, (4, 4, 16), (64, 16, 1), 0), buf6, out=buf9)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 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(buf10, (4, 4, 4, 4), (64, 16, 4, 1))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf11, primals_9, 256, grid=grid(256), stream=stream0)
        del primals_9
    return (buf11, primals_1, primals_2, primals_4, primals_6, primals_8, buf4, buf5, buf6, reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf8, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf2, (4, 1, 16), (16, 16, 1), 0), reinterpret_tensor(buf3, (4, 16, 1), (16, 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((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 1, 1), (4, 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
import torch.nn as nn


class NormalAttention_embedded_gaussian(nn.Module):

    def __init__(self, input_channel_num, k=4):
        super(NormalAttention_embedded_gaussian, self).__init__()
        self.c_in = input_channel_num
        self.query_conv = nn.Conv2d(in_channels=self.c_in, out_channels=
            self.c_in // k, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=self.c_in, out_channels=self.
            c_in // k, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=self.c_in, out_channels=
            self.c_in, kernel_size=1)
        self.gamma = nn.Conv2d(in_channels=self.c_in, out_channels=self.
            c_in, kernel_size=1)

    def forward(self, x):
        B, C, H, W = x.size()
        proj_query = self.query_conv(x).view(B, -1, H * W).permute(0, 2, 1)
        proj_key = self.key_conv(x).view(B, -1, H * W)
        energy = torch.bmm(proj_query, proj_key)
        energy = torch.exp(energy)
        energy_sum = torch.sum(energy, dim=2, keepdim=True)
        energy = energy / energy_sum
        proj_value = self.value_conv(x).view(B, -1, H * W)
        out = torch.bmm(proj_value, energy).view(B, C, H, W)
        out = self.gamma(out)
        return out


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


def get_init_inputs():
    return [[], {'input_channel_num': 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
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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_per_fused_div_exp_sum_1(in_ptr0, out_ptr0, out_ptr1, 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 = tl_math.exp(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = tmp1 / tmp5
    tl.store(out_ptr1 + (r1 + 16 * x0), tmp6, xmask)
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, 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_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)


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, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 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, 1, 4, 4), (16, 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, 1, 4, 4), (16, 16, 4, 1))
        buf2 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(64)](buf2, primals_3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        buf3 = reinterpret_tensor(buf1, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf1
        triton_poi_fused_convolution_0[grid(64)](buf3, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 16, 1), (16, 1, 0),
            0), reinterpret_tensor(buf3, (4, 1, 16), (16, 0, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        triton_per_fused_div_exp_sum_1[grid(64)](buf4, buf5, buf6, 64, 16,
            XBLOCK=32, num_warps=4, num_stages=1)
        buf7 = 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(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_2[grid(256)](buf8, primals_7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf9 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf8, (4, 4, 16), (64, 16, 1),
            0), buf6, out=buf9)
        buf10 = extern_kernels.convolution(reinterpret_tensor(buf9, (4, 4, 
            4, 4), (64, 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(buf10, (4, 4, 4, 4), (64, 16, 4, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_2[grid(256)](buf11, primals_9, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
    return (buf11, primals_1, primals_2, primals_4, primals_6, primals_8,
        buf4, buf5, buf6, reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4,
        1), 0), reinterpret_tensor(buf8, (4, 16, 4), (64, 1, 16), 0),
        reinterpret_tensor(buf2, (4, 1, 16), (16, 16, 1), 0),
        reinterpret_tensor(buf3, (4, 16, 1), (16, 1, 16), 0))


class NormalAttention_embedded_gaussianNew(nn.Module):

    def __init__(self, input_channel_num, k=4):
        super(NormalAttention_embedded_gaussianNew, self).__init__()
        self.c_in = input_channel_num
        self.query_conv = nn.Conv2d(in_channels=self.c_in, out_channels=
            self.c_in // k, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=self.c_in, out_channels=self.
            c_in // k, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=self.c_in, out_channels=
            self.c_in, kernel_size=1)
        self.gamma = nn.Conv2d(in_channels=self.c_in, out_channels=self.
            c_in, kernel_size=1)

    def forward(self, input_0):
        primals_2 = self.query_conv.weight
        primals_3 = self.query_conv.bias
        primals_4 = self.key_conv.weight
        primals_5 = self.key_conv.bias
        primals_6 = self.value_conv.weight
        primals_7 = self.value_conv.bias
        primals_8 = self.gamma.weight
        primals_9 = self.gamma.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]
