# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/y7/cy7oa3oifgsxcjwbu75kfgu6mbr6rjzt53mka577xofryqme3za3.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=2] = 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 = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 635040
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 15876) % 10
    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_6/inductor_cache/k4/ck4icz7eh76mil42q5htyliip4nlautz2elsqqu5e6sgcpvgxiu4.py
# Topologically Sorted Source Nodes: [max_pool2d, x], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
# Source node to ATen node mapping:
#   max_pool2d => _low_memory_max_pool2d_with_offsets, getitem_1
#   x => relu
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%convolution, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem,), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 158760
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 63
    x3 = (xindex // 63)
    x2 = (xindex // 3969)
    x4 = xindex % 3969
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (252*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (252*x3)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (126 + (2*x0) + (252*x3)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (127 + (2*x0) + (252*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)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + (x4 + (4096*x2)), tmp15, xmask)
    tl.store(out_ptr1 + (x5), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/f3/cf3y45pn4lj7xegtznu3afmkltwdkwr4prrt3jvxutxwh7t6ara3.py
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    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 = 297680
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3721) % 20
    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_6/inductor_cache/wk/cwkfnbfbquolembezt3p4b33slyi74tw4qsbjqou37vmcxdtnref.py
# Topologically Sorted Source Nodes: [max_pool2d_1, x_1], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   max_pool2d_1 => _low_memory_max_pool2d_with_offsets_1, getitem_3
#   x_1 => relu_1
# 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 = (%convolution_1, [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 = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_2,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_relu_threshold_backward_3(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 72000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 30
    x1 = (xindex // 30) % 30
    x5 = (xindex // 900)
    x3 = (xindex // 18000)
    x4 = xindex % 18000
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (122*x1) + (3721*x5)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (122*x1) + (3721*x5)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (61 + (2*x0) + (122*x1) + (3721*x5)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (62 + (2*x0) + (122*x1) + (3721*x5)), 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)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = 0.0
    tmp20 = tmp18 <= tmp19
    tl.store(out_ptr0 + (x4 + (18048*x3)), tmp15, xmask)
    tl.store(out_ptr1 + (x4 + (18016*x3)), tmp18, xmask)
    tl.store(out_ptr2 + (x4 + (18048*x3)), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/5d/c5dvgve24hbvrdcz5w4qrfj4r34h3se3x2w5fculeyheewlhp45h.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_3 => relu_2
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_7), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_4 = async_compile.triton('triton_poi_fused_relu_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=[512], 
    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_relu_4', '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_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 100
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4c/c4csmjdmicwy4zs334oschlbsbq5e3ook4zzhhhd5ipx6pnxvi54.py
# Topologically Sorted Source Nodes: [x_4, log_softmax], Original ATen: [aten.relu, aten._log_softmax, aten.threshold_backward]
# Source node to ATen node mapping:
#   log_softmax => amax, exp, log, sub, sub_1, sum_1
#   x_4 => relu_3
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_9), kwargs = {})
#   %relu_3 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%relu_3, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%relu_3, %amax), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_per_fused__log_softmax_relu_threshold_backward_5 = async_compile.triton('triton_per_fused__log_softmax_relu_threshold_backward_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.persistent_reduction(
    size_hints=[4, 8],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32', 5: '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__log_softmax_relu_threshold_backward_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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__log_softmax_relu_threshold_backward_5(in_ptr0, in_ptr1, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 8
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (8*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp5, float("-inf"))
    tmp8 = triton_helpers.max2(tmp7, 1)[:, None]
    tmp9 = tmp4 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp9 - tmp15
    tmp17 = 0.0
    tmp18 = tmp4 <= tmp17
    tl.store(out_ptr2 + (r1 + (8*x0)), tmp16, xmask)
    tl.store(out_ptr3 + (r1 + (8*x0)), tmp18, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (10, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (10, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 128, 128), (49152, 16384, 128, 1))
    assert_size_stride(primals_4, (20, 10, 3, 3), (90, 9, 3, 1))
    assert_size_stride(primals_5, (20, ), (1, ))
    assert_size_stride(primals_6, (100, 18000), (18000, 1))
    assert_size_stride(primals_7, (100, ), (1, ))
    assert_size_stride(primals_8, (8, 100), (100, 1))
    assert_size_stride(primals_9, (8, ), (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, 10, 126, 126), (158760, 15876, 126, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 635040, grid=grid(635040), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 10, 63, 63), (40960, 4096, 63, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 10, 63, 63), (39690, 3969, 63, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_pool2d, x], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
        triton_poi_fused_max_pool2d_with_indices_relu_1.run(buf1, buf2, buf3, 158760, grid=grid(158760), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 61, 61), (74420, 3721, 61, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf5, primals_5, 297680, grid=grid(297680), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((4, 20, 30, 30), (18048, 900, 30, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 20, 30, 30), (18016, 900, 30, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 20, 30, 30), (18048, 900, 30, 1), torch.bool)
        # Topologically Sorted Source Nodes: [max_pool2d_1, x_1], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.threshold_backward]
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3.run(buf5, buf6, buf7, buf15, 72000, grid=grid(72000), stream=stream0)
        buf8 = empty_strided_cuda((4, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 18000), (18016, 1), 0), reinterpret_tensor(primals_6, (18000, 100), (1, 18000), 0), out=buf8)
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu]
        triton_poi_fused_relu_4.run(buf9, primals_7, 400, grid=grid(400), stream=stream0)
        del primals_7
        buf10 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf9, reinterpret_tensor(primals_8, (100, 8), (1, 100), 0), out=buf10)
        buf13 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_4, log_softmax], Original ATen: [aten.relu, aten._log_softmax, aten.threshold_backward]
        triton_per_fused__log_softmax_relu_threshold_backward_5.run(buf10, primals_9, buf13, buf14, 4, 8, grid=grid(4), stream=stream0)
        del buf10
        del primals_9
    return (buf13, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5, buf6, reinterpret_tensor(buf7, (4, 18000), (18016, 1), 0), buf9, buf13, buf14, primals_8, primals_6, buf15, )


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((10, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 128, 128), (49152, 16384, 128, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((20, 10, 3, 3), (90, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((100, 18000), (18000, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((100, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((8, 100), (100, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


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

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


class Net(nn.Module):

    def __init__(self, num_classes=8):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=(3, 3))
        self.conv2 = nn.Conv2d(10, 20, kernel_size=(3, 3))
        nn.init.xavier_uniform_(self.conv1.weight)
        nn.init.xavier_uniform_(self.conv2.weight)
        self.conv1.bias.data.fill_(0.01)
        self.conv2.bias.data.fill_(0.01)
        self.fc1 = nn.Linear(in_features=18000, out_features=100)
        self.fc2 = nn.Linear(in_features=100, out_features=num_classes)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 18000)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        return torch.log_softmax(x, dim=1)


def get_inputs():
    return [torch.rand([4, 3, 128, 128])]


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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 635040
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 15876 % 10
    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_poi_fused_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 158760
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 63
    x3 = xindex // 63
    x2 = xindex // 3969
    x4 = xindex % 3969
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 252 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 252 * x3), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (126 + 2 * x0 + 252 * x3), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (127 + 2 * x0 + 252 * 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)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + (x4 + 4096 * x2), tmp15, xmask)
    tl.store(out_ptr1 + x5, tmp18, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 297680
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3721 % 20
    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_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3(in_ptr0,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 72000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 30
    x1 = xindex // 30 % 30
    x5 = xindex // 900
    x3 = xindex // 18000
    x4 = xindex % 18000
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 122 * x1 + 3721 * x5), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 122 * x1 + 3721 * x5), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (61 + 2 * x0 + 122 * x1 + 3721 * x5), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (62 + 2 * x0 + 122 * x1 + 3721 * x5), 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)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = 0.0
    tmp20 = tmp18 <= tmp19
    tl.store(out_ptr0 + (x4 + 18048 * x3), tmp15, xmask)
    tl.store(out_ptr1 + (x4 + 18016 * x3), tmp18, xmask)
    tl.store(out_ptr2 + (x4 + 18048 * x3), tmp20, xmask)


@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 100
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_per_fused__log_softmax_relu_threshold_backward_5(in_ptr0,
    in_ptr1, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 8 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp5, float('-inf'))
    tmp8 = triton_helpers.max2(tmp7, 1)[:, None]
    tmp9 = tmp4 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp9 - tmp15
    tmp17 = 0.0
    tmp18 = tmp4 <= tmp17
    tl.store(out_ptr2 + (r1 + 8 * x0), tmp16, xmask)
    tl.store(out_ptr3 + (r1 + 8 * x0), tmp18, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (10, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (10,), (1,))
    assert_size_stride(primals_3, (4, 3, 128, 128), (49152, 16384, 128, 1))
    assert_size_stride(primals_4, (20, 10, 3, 3), (90, 9, 3, 1))
    assert_size_stride(primals_5, (20,), (1,))
    assert_size_stride(primals_6, (100, 18000), (18000, 1))
    assert_size_stride(primals_7, (100,), (1,))
    assert_size_stride(primals_8, (8, 100), (100, 1))
    assert_size_stride(primals_9, (8,), (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, 10, 126, 126), (158760, 15876, 126, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(635040)](buf1, primals_2, 
            635040, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 10, 63, 63), (40960, 4096, 63, 1),
            torch.int8)
        buf3 = empty_strided_cuda((4, 10, 63, 63), (39690, 3969, 63, 1),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_1[grid(158760)](buf1,
            buf2, buf3, 158760, XBLOCK=512, num_warps=8, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 61, 61), (74420, 3721, 61, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(297680)](buf5, primals_5, 
            297680, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 20, 30, 30), (18048, 900, 30, 1),
            torch.int8)
        buf7 = empty_strided_cuda((4, 20, 30, 30), (18016, 900, 30, 1),
            torch.float32)
        buf15 = empty_strided_cuda((4, 20, 30, 30), (18048, 900, 30, 1),
            torch.bool)
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3[grid
            (72000)](buf5, buf6, buf7, buf15, 72000, XBLOCK=1024, num_warps
            =4, num_stages=1)
        buf8 = empty_strided_cuda((4, 100), (100, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 18000), (18016, 1), 
            0), reinterpret_tensor(primals_6, (18000, 100), (1, 18000), 0),
            out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_4[grid(400)](buf9, primals_7, 400, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_7
        buf10 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_8, (100, 8), (1,
            100), 0), out=buf10)
        buf13 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        triton_per_fused__log_softmax_relu_threshold_backward_5[grid(4)](buf10,
            primals_9, buf13, buf14, 4, 8, XBLOCK=1, num_warps=2, num_stages=1)
        del buf10
        del primals_9
    return (buf13, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
        buf6, reinterpret_tensor(buf7, (4, 18000), (18016, 1), 0), buf9,
        buf13, buf14, primals_8, primals_6, buf15)


class NetNew(nn.Module):

    def __init__(self, num_classes=8):
        super(NetNew, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=(3, 3))
        self.conv2 = nn.Conv2d(10, 20, kernel_size=(3, 3))
        nn.init.xavier_uniform_(self.conv1.weight)
        nn.init.xavier_uniform_(self.conv2.weight)
        self.conv1.bias.data.fill_(0.01)
        self.conv2.bias.data.fill_(0.01)
        self.fc1 = nn.Linear(in_features=18000, out_features=100)
        self.fc2 = nn.Linear(in_features=100, out_features=num_classes)

    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.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.weight
        primals_9 = 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])
        return output[0]
