# 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_8/inductor_cache/46/c463lktybvwsxd7jkazchfh6m36duntyrdz6ny64aafgs3ch4fov.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[16777216], 
    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_convolution_relu_0', 'mutated_arg_names': [], '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_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 14745600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 3600) % 1024
    x0 = xindex % 3600
    x4 = (xindex // 3600)
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + (3616*x4)), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/tn/ctnbx53utc53ahrmpy67atrztc3nkmijdes7yq57bezz5q564xcv.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_2 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 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_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 921600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 15
    x1 = (xindex // 15) % 15
    x2 = (xindex // 225)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (60 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (61 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (62 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (63 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (120 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (121 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (122 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (123 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (180 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (181 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (182 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (183 + (4*x0) + (240*x1) + (3616*x2)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp22 = triton_helpers.maximum(tmp21, tmp20)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tmp26 = triton_helpers.maximum(tmp25, tmp24)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp30 = triton_helpers.maximum(tmp29, tmp28)
    tmp31 = tmp1 > tmp0
    tmp32 = tl.full([1], 1, tl.int8)
    tmp33 = tl.full([1], 0, tl.int8)
    tmp34 = tl.where(tmp31, tmp32, tmp33)
    tmp35 = tmp3 > tmp2
    tmp36 = tl.full([1], 2, tl.int8)
    tmp37 = tl.where(tmp35, tmp36, tmp34)
    tmp38 = tmp5 > tmp4
    tmp39 = tl.full([1], 3, tl.int8)
    tmp40 = tl.where(tmp38, tmp39, tmp37)
    tmp41 = tmp7 > tmp6
    tmp42 = tl.full([1], 4, tl.int8)
    tmp43 = tl.where(tmp41, tmp42, tmp40)
    tmp44 = tmp9 > tmp8
    tmp45 = tl.full([1], 5, tl.int8)
    tmp46 = tl.where(tmp44, tmp45, tmp43)
    tmp47 = tmp11 > tmp10
    tmp48 = tl.full([1], 6, tl.int8)
    tmp49 = tl.where(tmp47, tmp48, tmp46)
    tmp50 = tmp13 > tmp12
    tmp51 = tl.full([1], 7, tl.int8)
    tmp52 = tl.where(tmp50, tmp51, tmp49)
    tmp53 = tmp15 > tmp14
    tmp54 = tl.full([1], 8, tl.int8)
    tmp55 = tl.where(tmp53, tmp54, tmp52)
    tmp56 = tmp17 > tmp16
    tmp57 = tl.full([1], 9, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp19 > tmp18
    tmp60 = tl.full([1], 10, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp21 > tmp20
    tmp63 = tl.full([1], 11, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp23 > tmp22
    tmp66 = tl.full([1], 12, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp25 > tmp24
    tmp69 = tl.full([1], 13, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp27 > tmp26
    tmp72 = tl.full([1], 14, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp29 > tmp28
    tmp75 = tl.full([1], 15, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + (x3), tmp30, None)
    tl.store(out_ptr1 + (x3), tmp76, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/2u/c2uphabntjycmy72y5yunapjsda35dxgiixb3kjy7gwbpsrwwmjz.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_3 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 8), 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=[262144], 
    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 = 230400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 225) % 256
    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_8/inductor_cache/jb/cjbsapjflsm5ba3v6xqsn7q7l57jyfb2d3372og5ewajuwyiptw5.py
# Topologically Sorted Source Nodes: [x_4, adaptive_avg_pool2d], Original ATen: [aten.convolution, aten.mean]
# Source node to ATen node mapping:
#   adaptive_avg_pool2d => mean
#   x_4 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 16), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%convolution_2, [-1, -2], True), kwargs = {})
triton_per_fused_convolution_mean_3 = async_compile.triton('triton_per_fused_convolution_mean_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.persistent_reduction(
    size_hints=[128, 256],
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_mean_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_mean_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    rnumel = 196
    RBLOCK: tl.constexpr = 256
    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 = rindex < rnumel
    r2 = rindex
    x3 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + (r2 + (196*x3)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 196.0
    tmp8 = tmp6 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x3), tmp8, 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 = args
    args.clear()
    assert_size_stride(primals_1, (1024, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (1024, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (256, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_5, (256, ), (1, ))
    assert_size_stride(primals_6, (32, 16, 2, 2), (64, 4, 2, 1))
    assert_size_stride(primals_7, (32, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1024, 60, 60), (3686400, 3600, 60, 1))
        buf1 = empty_strided_cuda((4, 1024, 60, 60), (3702784, 3616, 60, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf0, primals_2, buf1, 14745600, grid=grid(14745600), stream=stream0)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 1024, 15, 15), (230400, 225, 15, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 1024, 15, 15), (230400, 225, 15, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf1, buf2, buf3, 921600, grid=grid(921600), stream=stream0)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=8, bias=None)
        assert_size_stride(buf4, (4, 256, 15, 15), (57600, 225, 15, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf5, primals_5, 230400, grid=grid(230400), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=16, bias=None)
        assert_size_stride(buf6, (4, 32, 14, 14), (6272, 196, 14, 1))
        buf7 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32)
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [x_4, adaptive_avg_pool2d], Original ATen: [aten.convolution, aten.mean]
        triton_per_fused_convolution_mean_3.run(buf8, buf6, primals_7, 128, 196, grid=grid(128), stream=stream0)
        del buf6
        del primals_7
    return (reinterpret_tensor(buf8, (4, 32), (32, 1), 0), primals_1, primals_3, primals_4, primals_6, buf1, buf2, buf3, buf5, )


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((1024, 3, 5, 5), (75, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((256, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 16, 2, 2), (64, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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 nn
from torch.nn import functional as f


class TianzigeCNN(nn.Module):

    def __init__(self, dimension):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 1024, 5)
        self.relu = nn.ReLU(inplace=True)
        self.max_pool = nn.MaxPool2d(4)
        self.conv2 = nn.Conv2d(1024, 256, 1, groups=8)
        self.conv3 = nn.Conv2d(256, dimension, 2, groups=16)
        nn.init.kaiming_uniform_(self.conv1.weight)
        nn.init.kaiming_uniform_(self.conv2.weight)
        nn.init.kaiming_uniform_(self.conv3.weight)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.max_pool(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = f.adaptive_avg_pool2d(x, output_size=1).squeeze()
        return x


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


def get_init_inputs():
    return [[], {'dimension': 32}]

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 import 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_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 3600 % 1024
    x0 = xindex % 3600
    x4 = xindex // 3600
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + 3616 * x4), tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 15
    x1 = xindex // 15 % 15
    x2 = xindex // 225
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (60 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (61 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (62 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (63 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (120 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (121 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (122 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (123 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (180 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (181 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (182 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (183 + 4 * x0 + 240 * x1 + 3616 * x2), None,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp22 = triton_helpers.maximum(tmp21, tmp20)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tmp26 = triton_helpers.maximum(tmp25, tmp24)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp30 = triton_helpers.maximum(tmp29, tmp28)
    tmp31 = tmp1 > tmp0
    tmp32 = tl.full([1], 1, tl.int8)
    tmp33 = tl.full([1], 0, tl.int8)
    tmp34 = tl.where(tmp31, tmp32, tmp33)
    tmp35 = tmp3 > tmp2
    tmp36 = tl.full([1], 2, tl.int8)
    tmp37 = tl.where(tmp35, tmp36, tmp34)
    tmp38 = tmp5 > tmp4
    tmp39 = tl.full([1], 3, tl.int8)
    tmp40 = tl.where(tmp38, tmp39, tmp37)
    tmp41 = tmp7 > tmp6
    tmp42 = tl.full([1], 4, tl.int8)
    tmp43 = tl.where(tmp41, tmp42, tmp40)
    tmp44 = tmp9 > tmp8
    tmp45 = tl.full([1], 5, tl.int8)
    tmp46 = tl.where(tmp44, tmp45, tmp43)
    tmp47 = tmp11 > tmp10
    tmp48 = tl.full([1], 6, tl.int8)
    tmp49 = tl.where(tmp47, tmp48, tmp46)
    tmp50 = tmp13 > tmp12
    tmp51 = tl.full([1], 7, tl.int8)
    tmp52 = tl.where(tmp50, tmp51, tmp49)
    tmp53 = tmp15 > tmp14
    tmp54 = tl.full([1], 8, tl.int8)
    tmp55 = tl.where(tmp53, tmp54, tmp52)
    tmp56 = tmp17 > tmp16
    tmp57 = tl.full([1], 9, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp19 > tmp18
    tmp60 = tl.full([1], 10, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp21 > tmp20
    tmp63 = tl.full([1], 11, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp23 > tmp22
    tmp66 = tl.full([1], 12, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp25 > tmp24
    tmp69 = tl.full([1], 13, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp27 > tmp26
    tmp72 = tl.full([1], 14, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp29 > tmp28
    tmp75 = tl.full([1], 15, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + x3, tmp30, None)
    tl.store(out_ptr1 + x3, tmp76, None)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 230400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 225 % 256
    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_convolution_mean_3(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 196
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex
    x3 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + (r2 + 196 * x3), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 196.0
    tmp8 = tmp6 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (1024, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (1024,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (256, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (32, 16, 2, 2), (64, 4, 2, 1))
    assert_size_stride(primals_7, (32,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1024, 60, 60), (3686400, 3600, 60, 1))
        buf1 = empty_strided_cuda((4, 1024, 60, 60), (3702784, 3616, 60, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(14745600)](buf0, primals_2,
            buf1, 14745600, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 1024, 15, 15), (230400, 225, 15, 1),
            torch.float32)
        buf3 = empty_strided_cuda((4, 1024, 15, 15), (230400, 225, 15, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(921600)](buf1, buf2,
            buf3, 921600, XBLOCK=512, num_warps=8, num_stages=1)
        buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=8, bias=None)
        assert_size_stride(buf4, (4, 256, 15, 15), (57600, 225, 15, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(230400)](buf5, primals_5, 
            230400, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=16, bias=None)
        assert_size_stride(buf6, (4, 32, 14, 14), (6272, 196, 14, 1))
        buf7 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf8 = buf7
        del buf7
        triton_per_fused_convolution_mean_3[grid(128)](buf8, buf6,
            primals_7, 128, 196, XBLOCK=1, num_warps=2, num_stages=1)
        del buf6
        del primals_7
    return reinterpret_tensor(buf8, (4, 32), (32, 1), 0
        ), primals_1, primals_3, primals_4, primals_6, buf1, buf2, buf3, buf5


class TianzigeCNNNew(nn.Module):

    def __init__(self, dimension):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 1024, 5)
        self.relu = nn.ReLU(inplace=True)
        self.max_pool = nn.MaxPool2d(4)
        self.conv2 = nn.Conv2d(1024, 256, 1, groups=8)
        self.conv3 = nn.Conv2d(256, dimension, 2, groups=16)
        nn.init.kaiming_uniform_(self.conv1.weight)
        nn.init.kaiming_uniform_(self.conv2.weight)
        nn.init.kaiming_uniform_(self.conv3.weight)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
