# 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_1/inductor_cache/5k/c5knxaihek7fapvcfd2krvazvh5kfwszu4bdb2ebly5gz25i3mjm.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   x => expm1, gt, mul, mul_2, where
# Graph fragment:
#   %convolution : [num_users=3] = 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 = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
triton_poi_fused_convolution_elu_0 = async_compile.triton('triton_poi_fused_convolution_elu_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_elu_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 115200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 900) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ya/cyatzwbxlw45sv4vwidfqbgxhwzlexlvqv7a2eg6amse2ud6duqn.py
# Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   x_1 => expm1_1, gt_1, mul_3, mul_5, where_1
# Graph fragment:
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_3,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_3, %mul_5), kwargs = {})
triton_poi_fused_convolution_elu_1 = async_compile.triton('triton_poi_fused_convolution_elu_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_elu_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 100352
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 784) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x3), tmp2, None)
    tl.store(out_ptr0 + (x3), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/fz/cfzg3qdwec5lstpepdjdudu5tdtu2fyvlwlz5bpalyqxa7muzgwa.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_2 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_2(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 14
    x1 = (xindex // 14)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (56*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (56*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (28 + (2*x0) + (56*x1)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (29 + (2*x0) + (56*x1)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
    tl.store(out_ptr1 + (x2), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/4a/c4adbs2zvcyhpyarft7tzdybxfw5cze3dac4gasti4di6pcpppuz.py
# Topologically Sorted Source Nodes: [conv2d_2, x_3], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   x_3 => expm1_2, gt_2, mul_6, mul_8, where_2
# Graph fragment:
#   %convolution_2 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_6 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 1.0), kwargs = {})
#   %expm1_2 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_6,), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_2, 1.0), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %mul_6, %mul_8), kwargs = {})
triton_poi_fused_convolution_elu_3 = async_compile.triton('triton_poi_fused_convolution_elu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_elu_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 36864
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 144) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x3), tmp2, None)
    tl.store(out_ptr0 + (x3), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/e5/ce5fcyhq5omwppt7pa4tcux5mt66igvmzxuh5h7bzff7bs5ueaj2.py
# Topologically Sorted Source Nodes: [conv2d_3, x_4], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
#   x_4 => expm1_3, gt_3, mul_11, mul_9, where_3
# Graph fragment:
#   %convolution_3 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_9 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, 1.0), kwargs = {})
#   %expm1_3 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_9,), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_3, 1.0), kwargs = {})
#   %where_3 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %mul_9, %mul_11), kwargs = {})
triton_poi_fused_convolution_elu_4 = async_compile.triton('triton_poi_fused_convolution_elu_4', '''
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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_elu_4(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 25600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 100) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ye/cyewf77lsr5xfweqavruswxzhu3mdxwitxnuanqdhbuci32jubws.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_5 => _low_memory_max_pool2d_with_offsets_1, getitem_3
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%where_3, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_5 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_5', '''
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: '*i8', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_5(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x3 = (xindex // 5)
    x2 = (xindex // 1600)
    x4 = xindex % 1600
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (20*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (20*x3)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (10 + (2*x0) + (20*x3)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (11 + (2*x0) + (20*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x4 + (1664*x2)), tmp15, xmask)
    tl.store(out_ptr1 + (x5), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/4e/c4e6fl7aylmsjnu2tb2lgahthzjhg4vmyqnneoc6rsbcjcaxzeoy.py
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_7 => expm1_4, gt_4, mul_12, mul_14, where_4
# Graph fragment:
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%addmm, 0), kwargs = {})
#   %mul_12 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%addmm, 1.0), kwargs = {})
#   %expm1_4 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_12,), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_4, 1.0), kwargs = {})
#   %where_4 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %mul_12, %mul_14), kwargs = {})
triton_poi_fused_elu_6 = async_compile.triton('triton_poi_fused_elu_6', '''
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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_elu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_elu_6(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0), tmp7, None)
''', 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, primals_10, primals_11, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (32, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 32, 32), (3072, 1024, 32, 1))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (512, 1600), (1600, 1))
    assert_size_stride(primals_11, (512, ), (1, ))
    assert_size_stride(primals_12, (10, 512), (512, 1))
    assert_size_stride(primals_13, (10, ), (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_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, 32, 30, 30), (28800, 900, 30, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 32, 30, 30), (28800, 900, 30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.elu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_elu_0.run(buf1, primals_2, buf2, 115200, grid=grid(115200), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 28, 28), (25088, 784, 28, 1))
        buf4 = buf3; del buf3  # reuse
        buf5 = empty_strided_cuda((4, 32, 28, 28), (25088, 784, 28, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_1.run(buf4, primals_5, buf5, 100352, grid=grid(100352), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((4, 32, 14, 14), (6272, 196, 14, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 32, 14, 14), (6272, 196, 14, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_2.run(buf5, buf6, buf7, 25088, grid=grid(25088), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 12, 12), (9216, 144, 12, 1))
        buf9 = buf8; del buf8  # reuse
        buf10 = empty_strided_cuda((4, 64, 12, 12), (9216, 144, 12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_2, x_3], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_3.run(buf9, primals_7, buf10, 36864, grid=grid(36864), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 64, 10, 10), (6400, 100, 10, 1))
        buf12 = buf11; del buf11  # reuse
        buf13 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_3, x_4], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_4.run(buf12, primals_9, buf13, 25600, grid=grid(25600), stream=stream0)
        del primals_9
        buf14 = empty_strided_cuda((4, 64, 5, 5), (1664, 25, 5, 1), torch.int8)
        buf15 = empty_strided_cuda((4, 64, 5, 5), (1600, 25, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_5.run(buf13, buf14, buf15, 6400, grid=grid(6400), stream=stream0)
        buf16 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf15, (4, 1600), (1600, 1), 0), reinterpret_tensor(primals_10, (1600, 512), (1, 1600), 0), alpha=1, beta=1, out=buf16)
        del primals_11
        buf17 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.elu]
        triton_poi_fused_elu_6.run(buf16, buf17, 2048, grid=grid(2048), stream=stream0)
        buf18 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_13, buf17, reinterpret_tensor(primals_12, (512, 10), (1, 512), 0), alpha=1, beta=1, out=buf18)
        del primals_13
    return (buf18, primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf2, buf4, buf5, buf6, buf7, buf9, buf10, buf12, buf13, buf14, reinterpret_tensor(buf15, (4, 1600), (1600, 1), 0), buf16, buf17, primals_12, primals_10, )


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((32, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 32, 32), (3072, 1024, 32, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((512, 1600), (1600, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((10, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((10, ), (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, primals_10, primals_11, primals_12, primals_13])
    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


class Net1(nn.Module):

    def __init__(self):
        super(Net1, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.conv2 = nn.Conv2d(32, 32, 3)
        self.conv3 = nn.Conv2d(32, 64, 3)
        self.conv4 = nn.Conv2d(64, 64, 3)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 5 * 5, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = F.elu(self.conv1(x))
        x = F.elu(self.conv2(x))
        x = self.pool1(x)
        x = F.elu(self.conv3(x))
        x = F.elu(self.conv4(x))
        x = self.pool2(x)
        x = x.view(-1, 64 * 5 * 5)
        x = F.elu(self.fc1(x))
        x = self.fc2(x)
        return x

    def linear_layer_ids(self):
        return [8, 10]

    def linear_layer_parameters(self):
        linear1 = torch.cat([x.view(-1) for x in self.fc1.parameters() or
            self.fc2.parameters()])
        return linear1

    def train_order_block_ids(self):
        return [[4, 5], [10, 11], [2, 3], [6, 7], [0, 1], [8, 9]]


def get_inputs():
    return [torch.rand([4, 3, 32, 32])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
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_elu_0(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 115200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 900 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, 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 // 784 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp9, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 14
    x1 = xindex // 14
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 56 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 56 * x1), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (28 + 2 * x0 + 56 * x1), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (29 + 2 * x0 + 56 * x1), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_elu_3(in_out_ptr0, in_ptr0, 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 // 144 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_elu_4(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 25600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 100 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 6400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x3 = xindex // 5
    x2 = xindex // 1600
    x4 = xindex % 1600
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 20 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 20 * x3), xmask, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr0 + (10 + 2 * x0 + 20 * x3), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (11 + 2 * x0 + 20 * x3), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x4 + 1664 * x2), tmp15, xmask)
    tl.store(out_ptr1 + x5, tmp16, xmask)


@triton.jit
def triton_poi_fused_elu_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 3, 32, 32), (3072, 1024, 32, 1))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (512, 1600), (1600, 1))
    assert_size_stride(primals_11, (512,), (1,))
    assert_size_stride(primals_12, (10, 512), (512, 1))
    assert_size_stride(primals_13, (10,), (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, 32, 30, 30), (28800, 900, 30, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 32, 30, 30), (28800, 900, 30, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_elu_0[grid(115200)](buf1, primals_2,
            buf2, 115200, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 28, 28), (25088, 784, 28, 1))
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 32, 28, 28), (25088, 784, 28, 1),
            torch.float32)
        triton_poi_fused_convolution_elu_1[grid(100352)](buf4, primals_5,
            buf5, 100352, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 32, 14, 14), (6272, 196, 14, 1),
            torch.float32)
        buf7 = empty_strided_cuda((4, 32, 14, 14), (6272, 196, 14, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_2[grid(25088)](buf5, buf6,
            buf7, 25088, XBLOCK=128, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 12, 12), (9216, 144, 12, 1))
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((4, 64, 12, 12), (9216, 144, 12, 1),
            torch.float32)
        triton_poi_fused_convolution_elu_3[grid(36864)](buf9, primals_7,
            buf10, 36864, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 64, 10, 10), (6400, 100, 10, 1))
        buf12 = buf11
        del buf11
        buf13 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1),
            torch.float32)
        triton_poi_fused_convolution_elu_4[grid(25600)](buf12, primals_9,
            buf13, 25600, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf14 = empty_strided_cuda((4, 64, 5, 5), (1664, 25, 5, 1), torch.int8)
        buf15 = empty_strided_cuda((4, 64, 5, 5), (1600, 25, 5, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_5[grid(6400)](buf13, buf14,
            buf15, 6400, XBLOCK=256, num_warps=4, num_stages=1)
        buf16 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf15, (4, 1600
            ), (1600, 1), 0), reinterpret_tensor(primals_10, (1600, 512), (
            1, 1600), 0), alpha=1, beta=1, out=buf16)
        del primals_11
        buf17 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        triton_poi_fused_elu_6[grid(2048)](buf16, buf17, 2048, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf18 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf17, reinterpret_tensor(
            primals_12, (512, 10), (1, 512), 0), alpha=1, beta=1, out=buf18)
        del primals_13
    return (buf18, primals_1, primals_3, primals_4, primals_6, primals_8,
        buf1, buf2, buf4, buf5, buf6, buf7, buf9, buf10, buf12, buf13,
        buf14, reinterpret_tensor(buf15, (4, 1600), (1600, 1), 0), buf16,
        buf17, primals_12, primals_10)


class Net1New(nn.Module):

    def __init__(self):
        super(Net1New, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.conv2 = nn.Conv2d(32, 32, 3)
        self.conv3 = nn.Conv2d(32, 64, 3)
        self.conv4 = nn.Conv2d(64, 64, 3)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 5 * 5, 512)
        self.fc2 = nn.Linear(512, 10)

    def linear_layer_ids(self):
        return [8, 10]

    def linear_layer_parameters(self):
        linear1 = torch.cat([x.view(-1) for x in self.fc1.parameters() or
            self.fc2.parameters()])
        return linear1

    def train_order_block_ids(self):
        return [[4, 5], [10, 11], [2, 3], [6, 7], [0, 1], [8, 9]]

    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_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.fc1.weight
        primals_11 = self.fc1.bias
        primals_12 = self.fc2.weight
        primals_13 = self.fc2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
