# 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/oo/coou5wvggqufudaduklp7zml27ok3omo7yvnss7pdphcm5ivci7x.py
# Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   output => convolution
#   output_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=[65536], 
    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 = 61504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3844) % 4
    x0 = xindex % 3844
    x4 = (xindex // 3844)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, 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 + (3872*x4)), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vd/cvdw7jx6mgetumoskb4iy5mzewggo3cgb7y56aps62yvsbhhwy4w.py
# Topologically Sorted Source Nodes: [output_2], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   output_2 => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%relu, [5, 5], [3, 3]), kwargs = {})
triton_poi_fused_avg_pool2d_1 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[8192], 
    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_avg_pool2d_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 25, '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_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = (xindex // 20) % 20
    x2 = (xindex // 400)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (62 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (63 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (64 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (65 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (66 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (124 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (125 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (126 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (127 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (128 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (186 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (187 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (188 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr0 + (189 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (190 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr0 + (248 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr0 + (249 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr0 + (250 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr0 + (251 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr0 + (252 + (3*x0) + (186*x1) + (3872*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp8 = tmp7 + tmp6
    tmp10 = tmp9 + tmp8
    tmp12 = tmp11 + tmp10
    tmp14 = tmp13 + tmp12
    tmp16 = tmp15 + tmp14
    tmp18 = tmp17 + tmp16
    tmp20 = tmp19 + tmp18
    tmp22 = tmp21 + tmp20
    tmp24 = tmp23 + tmp22
    tmp26 = tmp25 + tmp24
    tmp28 = tmp27 + tmp26
    tmp30 = tmp29 + tmp28
    tmp32 = tmp31 + tmp30
    tmp34 = tmp33 + tmp32
    tmp36 = tmp35 + tmp34
    tmp38 = tmp37 + tmp36
    tmp40 = tmp39 + tmp38
    tmp42 = tmp41 + tmp40
    tmp44 = tmp43 + tmp42
    tmp46 = tmp45 + tmp44
    tmp48 = tmp47 + tmp46
    tmp49 = 0.04
    tmp50 = tmp48 * tmp49
    tl.store(out_ptr0 + (x3), tmp50, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [output], 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, 4, 62, 62), (15376, 3844, 62, 1))
        buf1 = empty_strided_cuda((4, 4, 62, 62), (15488, 3872, 62, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf0, primals_2, buf1, 61504, grid=grid(61504), stream=stream0)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 20, 20), (1600, 400, 20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_2], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_1.run(buf1, buf2, 6400, grid=grid(6400), stream=stream0)
    return (buf2, primals_1, primals_3, buf1, )


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, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 64, 64), (16384, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 CNN64x3(nn.Module):

    def __init__(self, input_channels, output_channels):
        super(CNN64x3, self).__init__()
        self.conv = nn.Conv2d(in_channels=input_channels, kernel_size=3,
            out_channels=output_channels)
        self.relu = nn.ReLU()
        self.pool = nn.AvgPool2d(5, stride=3, padding=0)

    def forward(self, batch_data):
        output = self.conv(batch_data)
        output = self.relu(output)
        output = self.pool(output)
        return output


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


def get_init_inputs():
    return [[], {'input_channels': 4, 'output_channels': 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
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


@triton.jit
def triton_poi_fused_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 61504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 4
    x0 = xindex % 3844
    x4 = xindex // 3844
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, 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 + 3872 * x4), tmp4, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 6400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = xindex // 20 % 20
    x2 = xindex // 400
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (62 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (63 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (64 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (65 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (66 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (124 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (125 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (126 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (127 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (128 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (186 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (187 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (188 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr0 + (189 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (190 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr0 + (248 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr0 + (249 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr0 + (250 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr0 + (251 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr0 + (252 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp8 = tmp7 + tmp6
    tmp10 = tmp9 + tmp8
    tmp12 = tmp11 + tmp10
    tmp14 = tmp13 + tmp12
    tmp16 = tmp15 + tmp14
    tmp18 = tmp17 + tmp16
    tmp20 = tmp19 + tmp18
    tmp22 = tmp21 + tmp20
    tmp24 = tmp23 + tmp22
    tmp26 = tmp25 + tmp24
    tmp28 = tmp27 + tmp26
    tmp30 = tmp29 + tmp28
    tmp32 = tmp31 + tmp30
    tmp34 = tmp33 + tmp32
    tmp36 = tmp35 + tmp34
    tmp38 = tmp37 + tmp36
    tmp40 = tmp39 + tmp38
    tmp42 = tmp41 + tmp40
    tmp44 = tmp43 + tmp42
    tmp46 = tmp45 + tmp44
    tmp48 = tmp47 + tmp46
    tmp49 = 0.04
    tmp50 = tmp48 * tmp49
    tl.store(out_ptr0 + x3, tmp50, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 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, 4, 62, 62), (15376, 3844, 62, 1))
        buf1 = empty_strided_cuda((4, 4, 62, 62), (15488, 3872, 62, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(61504)](buf0, primals_2,
            buf1, 61504, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 20, 20), (1600, 400, 20, 1), torch
            .float32)
        triton_poi_fused_avg_pool2d_1[grid(6400)](buf1, buf2, 6400, XBLOCK=
            256, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_3, buf1


class CNN64x3New(nn.Module):

    def __init__(self, input_channels, output_channels):
        super(CNN64x3New, self).__init__()
        self.conv = nn.Conv2d(in_channels=input_channels, kernel_size=3,
            out_channels=output_channels)
        self.relu = nn.ReLU()
        self.pool = nn.AvgPool2d(5, stride=3, padding=0)

    def forward(self, input_0):
        primals_1 = self.conv.weight
        primals_2 = self.conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
