# 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_4/inductor_cache/lj/clj3mscf4tqz7wlbw6be5v4cyjekfwhnnfmmz6ngsoi4fvpbcy27.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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_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 = 144000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3600) % 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_4/inductor_cache/xr/cxr65jdasvpgnteicbgqvfut76mmpllnjbf3v2jm2v2roq4iu75d.py
# Topologically Sorted Source Nodes: [max_pool2d, x, log_softmax], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => amax
#   max_pool2d => _low_memory_max_pool2d_with_offsets
#   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 = {})
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%getitem,), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%relu, [1], True), kwargs = {})
triton_per_fused__log_softmax_max_pool2d_with_indices_relu_1 = async_compile.triton('triton_per_fused__log_softmax_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.persistent_reduction(
    size_hints=[4096, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_per_fused__log_softmax_max_pool2d_with_indices_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__log_softmax_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 3600
    rnumel = 10
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r3 = rindex
    x0 = xindex % 30
    x1 = (xindex // 30) % 30
    x2 = (xindex // 900)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (120*x1) + (3600*r3) + (36000*x2)), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (120*x1) + (3600*r3) + (36000*x2)), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp3 = tl.load(in_ptr0 + (60 + (2*x0) + (120*x1) + (3600*r3) + (36000*x2)), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp5 = tl.load(in_ptr0 + (61 + (2*x0) + (120*x1) + (3600*r3) + (36000*x2)), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tl.full([1, 1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(rmask & xmask, tmp9, float("-inf"))
    tmp12 = triton_helpers.max2(tmp11, 1)[:, None]
    tl.store(out_ptr0 + (x4), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fb/cfbeya5mpnm5mpeblt7ld5jt6zk7bs4vdxj7yqyw32uw7faqhzrc.py
# Topologically Sorted Source Nodes: [max_pool2d, x, log_softmax], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten._log_softmax, aten.threshold_backward]
# Source node to ATen node mapping:
#   log_softmax => sub
#   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=3] = call_function[target=torch.ops.aten.relu.default](args = (%getitem,), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%relu, %amax), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused__log_softmax_max_pool2d_with_indices_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused__log_softmax_max_pool2d_with_indices_relu_threshold_backward_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: '*fp32', 4: '*i1', 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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_max_pool2d_with_indices_relu_threshold_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__log_softmax_max_pool2d_with_indices_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 36000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 30
    x5 = (xindex // 30)
    x2 = (xindex // 9000)
    x6 = xindex % 9000
    x7 = xindex % 900
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (120*x5)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (120*x5)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (60 + (2*x0) + (120*x5)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (61 + (2*x0) + (120*x5)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (x7 + (900*x2)), 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)
    tmp20 = tmp18 - tmp19
    tmp21 = 0.0
    tmp22 = tmp18 <= tmp21
    tl.store(out_ptr0 + (x6 + (9088*x2)), tmp15, xmask)
    tl.store(out_ptr1 + (x6 + (9024*x2)), tmp20, xmask)
    tl.store(out_ptr2 + (x6 + (9088*x2)), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ft/cft4mlnf34zpqfnf5ewqhl7tp5sqb7erbzskwdcruy33ojoi5cll.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => exp, sum_1
# Graph fragment:
#   %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 = {})
triton_per_fused__log_softmax_3 = async_compile.triton('triton_per_fused__log_softmax_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.persistent_reduction(
    size_hints=[4096, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_per_fused__log_softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__log_softmax_3(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 3600
    rnumel = 10
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r2 = rindex
    x0 = xindex % 900
    x1 = (xindex // 900)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (900*r2) + (9024*x1)), rmask & xmask, other=0.0)
    tmp1 = tl_math.exp(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask & xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr0 + (x3), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hz/chzybqdix7n6nxl3ea723vdxr6yps7cxjnu3t4yyxcjzbcjkinoc.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => log, sub_1
# Graph fragment:
#   %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 = {})
triton_poi_fused__log_softmax_4 = async_compile.triton('triton_poi_fused__log_softmax_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=[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__log_softmax_4', '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__log_softmax_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 36000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 9000)
    x3 = xindex % 9000
    x0 = xindex % 900
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (9024*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (900*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.log(tmp1)
    tmp3 = tmp0 - tmp2
    tl.store(out_ptr0 + (x4), tmp3, 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, (10, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_2, (10, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 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, 60, 60), (36000, 3600, 60, 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, 144000, grid=grid(144000), stream=stream0)
        del primals_2
        buf3 = empty_strided_cuda((4, 1, 30, 30), (900, 3616, 30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_pool2d, x, log_softmax], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten._log_softmax]
        triton_per_fused__log_softmax_max_pool2d_with_indices_relu_1.run(buf1, buf3, 3600, 10, grid=grid(3600), stream=stream0)
        buf2 = empty_strided_cuda((4, 10, 30, 30), (9088, 900, 30, 1), torch.int8)
        buf4 = empty_strided_cuda((4, 10, 30, 30), (9024, 900, 30, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 10, 30, 30), (9088, 900, 30, 1), torch.bool)
        # Topologically Sorted Source Nodes: [max_pool2d, x, log_softmax], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten._log_softmax, aten.threshold_backward]
        triton_poi_fused__log_softmax_max_pool2d_with_indices_relu_threshold_backward_2.run(buf1, buf3, buf2, buf4, buf7, 36000, grid=grid(36000), stream=stream0)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
        triton_per_fused__log_softmax_3.run(buf4, buf5, 3600, 10, grid=grid(3600), stream=stream0)
        buf6 = empty_strided_cuda((4, 10, 30, 30), (9000, 900, 30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_4.run(buf4, buf5, buf6, 36000, grid=grid(36000), stream=stream0)
        del buf4
        del buf5
    return (buf6, primals_1, primals_3, buf1, buf2, buf6, buf7, )


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, 1, 5, 5), (25, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64, 64), (4096, 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 logging
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


class BaseModel(nn.Module):
    """
    Base class for all models
    All models require an initialization and forward method, and __str__ is what is shown when print(model) is called
    """

    def __init__(self):
        super(BaseModel, self).__init__()
        self.logger = logging.getLogger(self.__class__.__name__)

    def forward(self, *input):
        """
        Forward pass logic

        :return: Model output
        """
        raise NotImplementedError

    def __str__(self):
        """
        Model prints with number of trainable parameters
        """
        model_parameters = filter(lambda p: p.requires_grad, self.parameters())
        params = sum([np.prod(p.size()) for p in model_parameters])
        return super(BaseModel, self).__str__(
            ) + '\nTrainable parameters: {}'.format(params)


class myCustomModel(BaseModel):

    def __init__(self, num_classes=2):
        super(myCustomModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        return F.log_softmax(x, dim=1)


def get_inputs():
    return [torch.rand([4, 1, 64, 64])]


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 logging
import numpy as np
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 144000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3600 % 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_per_fused__log_softmax_max_pool2d_with_indices_relu_1(in_ptr0,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 3600
    rnumel = 10
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r3 = rindex
    x0 = xindex % 30
    x1 = xindex // 30 % 30
    x2 = xindex // 900
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 120 * x1 + 3600 * r3 + 36000 * x2), 
        rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 120 * x1 + 3600 * r3 + 36000 *
        x2), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp3 = tl.load(in_ptr0 + (60 + 2 * x0 + 120 * x1 + 3600 * r3 + 36000 *
        x2), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp5 = tl.load(in_ptr0 + (61 + 2 * x0 + 120 * x1 + 3600 * r3 + 36000 *
        x2), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tl.full([1, 1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(rmask & xmask, tmp9, float('-inf'))
    tmp12 = triton_helpers.max2(tmp11, 1)[:, None]
    tl.store(out_ptr0 + x4, tmp12, xmask)


@triton.jit
def triton_poi_fused__log_softmax_max_pool2d_with_indices_relu_threshold_backward_2(
    in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 36000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 30
    x5 = xindex // 30
    x2 = xindex // 9000
    x6 = xindex % 9000
    x7 = xindex % 900
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 120 * x5), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 120 * x5), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (60 + 2 * x0 + 120 * x5), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (61 + 2 * x0 + 120 * x5), xmask,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (x7 + 900 * x2), 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)
    tmp20 = tmp18 - tmp19
    tmp21 = 0.0
    tmp22 = tmp18 <= tmp21
    tl.store(out_ptr0 + (x6 + 9088 * x2), tmp15, xmask)
    tl.store(out_ptr1 + (x6 + 9024 * x2), tmp20, xmask)
    tl.store(out_ptr2 + (x6 + 9088 * x2), tmp22, xmask)


@triton.jit
def triton_per_fused__log_softmax_3(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 3600
    rnumel = 10
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex
    x0 = xindex % 900
    x1 = xindex // 900
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 900 * r2 + 9024 * x1), rmask & xmask,
        other=0.0)
    tmp1 = tl_math.exp(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask & xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp5, xmask)


@triton.jit
def triton_poi_fused__log_softmax_4(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 36000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 9000
    x3 = xindex % 9000
    x0 = xindex % 900
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 9024 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 900 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl_math.log(tmp1)
    tmp3 = tmp0 - tmp2
    tl.store(out_ptr0 + x4, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (10, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_2, (10,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 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, 10, 60, 60), (36000, 3600, 60, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(144000)](buf1, primals_2, 
            144000, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 1, 30, 30), (900, 3616, 30, 1), torch
            .float32)
        triton_per_fused__log_softmax_max_pool2d_with_indices_relu_1[grid(3600)
            ](buf1, buf3, 3600, 10, XBLOCK=8, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 10, 30, 30), (9088, 900, 30, 1),
            torch.int8)
        buf4 = empty_strided_cuda((4, 10, 30, 30), (9024, 900, 30, 1),
            torch.float32)
        buf7 = empty_strided_cuda((4, 10, 30, 30), (9088, 900, 30, 1),
            torch.bool)
        triton_poi_fused__log_softmax_max_pool2d_with_indices_relu_threshold_backward_2[
            grid(36000)](buf1, buf3, buf2, buf4, buf7, 36000, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = buf3
        del buf3
        triton_per_fused__log_softmax_3[grid(3600)](buf4, buf5, 3600, 10,
            XBLOCK=32, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((4, 10, 30, 30), (9000, 900, 30, 1),
            torch.float32)
        triton_poi_fused__log_softmax_4[grid(36000)](buf4, buf5, buf6, 
            36000, XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        del buf5
    return buf6, primals_1, primals_3, buf1, buf2, buf6, buf7


class BaseModel(nn.Module):
    """
    Base class for all models
    All models require an initialization and forward method, and __str__ is what is shown when print(model) is called
    """

    def __init__(self):
        super(BaseModel, self).__init__()
        self.logger = logging.getLogger(self.__class__.__name__)

    def forward(self, *input):
        """
        Forward pass logic

        :return: Model output
        """
        raise NotImplementedError

    def __str__(self):
        """
        Model prints with number of trainable parameters
        """
        model_parameters = filter(lambda p: p.requires_grad, self.parameters())
        params = sum([np.prod(p.size()) for p in model_parameters])
        return super(BaseModel, self).__str__(
            ) + '\nTrainable parameters: {}'.format(params)


class myCustomModelNew(BaseModel):

    def __init__(self, num_classes=2):
        super(myCustomModelNew, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
