# 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/75/c75stkyqnnq2pq6p5hr3a43vinco6xw7twz6swa4efyrxbn4v27s.py
# Topologically Sorted Source Nodes: [conv2d, conv2d_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
#   conv2d_2 => convolution_2
# 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 = {})
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_10, %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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, '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_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 492032
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3844) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(in_out_ptr1 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/of/cofajujcm7nkdximos6acfsdvirn2ggrmubu52eiocefnxfpsjnp.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=[131072], 
    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, 3), 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 = 123008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 31
    x3 = (xindex // 31)
    x2 = (xindex // 30752)
    x4 = xindex % 30752
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (124*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (124*x3)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (62 + (2*x0) + (124*x3)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (63 + (2*x0) + (124*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 + (30848*x2)), tmp15, xmask)
    tl.store(out_ptr1 + (x5), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/wy/cwymbwwptdb4ddrrhb55pjwimim3qad6vgakzs46aqblanoqcb3g.py
# Topologically Sorted Source Nodes: [conv2d_1, conv2d_3], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   conv2d_3 => convolution_3
# 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 = {})
#   %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, '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_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 215296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 841) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(in_out_ptr1 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/6u/c6utkx4mv7gwq4xeag4t234ufjbsr5uz5rql7jjj5bf3zpmy5exl.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_5 : [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=[65536], 
    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 = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 14
    x1 = (xindex // 14) % 14
    x2 = (xindex // 196)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (58*x1) + (841*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (58*x1) + (841*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (29 + (2*x0) + (58*x1) + (841*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (30 + (2*x0) + (58*x1) + (841*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)
    tmp19 = 0.0
    tmp20 = tmp18 <= tmp19
    tl.store(out_ptr0 + (x3), tmp15, xmask)
    tl.store(out_ptr1 + (x3), tmp18, xmask)
    tl.store(out_ptr2 + (x3), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/dq/cdqtvanz2din737nabtkokrhopta6gnwkr4pr63rdph5v657a3zf.py
# Topologically Sorted Source Nodes: [x1_1, x2_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x1_1 => relu_2
#   x2_1 => relu_5
# Graph fragment:
#   %add_tensor_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_7), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_2,), kwargs = {})
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_7), kwargs = {})
#   %relu_5 : [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=[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_relu_4', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, '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_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 39200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/k3/ck3bp6ksgiytvmuvpqj47fe5vqklgsndxsfyvb3vjwo6bvbztco6.py
# Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.cat, aten.relu]
# Source node to ATen node mapping:
#   x_4 => cat
#   x_5 => relu_6
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%addmm_1, %addmm_3], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%cat,), kwargs = {})
triton_poi_fused_cat_relu_5 = async_compile.triton('triton_poi_fused_cat_relu_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=[4096], 
    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_cat_relu_5', '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_cat_relu_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = (xindex // 20)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 10, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((10*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 20, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((10*x1) + ((-10) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tl.store(out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ql/cql5vlc3dkywa4zch5tmvtrwf27k6oymkdkodetn3gle5tcbzd4t.py
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   x_6 => sigmoid
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_12), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_sigmoid_6 = async_compile.triton('triton_poi_fused_sigmoid_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=[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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_6', '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_sigmoid_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 392
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, 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, primals_10, primals_11, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (32, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (32, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (200, 256), (256, 1))
    assert_size_stride(primals_7, (200, ), (1, ))
    assert_size_stride(primals_8, (10, 200), (200, 1))
    assert_size_stride(primals_9, (10, ), (1, ))
    assert_size_stride(primals_10, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_11, (2, 20), (20, 1))
    assert_size_stride(primals_12, (2, ), (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, 62, 62), (123008, 3844, 62, 1))
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(primals_10, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 32, 62, 62), (123008, 3844, 62, 1))
        buf1 = buf0; del buf0  # reuse
        buf12 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [conv2d, conv2d_2], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, buf12, primals_2, 492032, grid=grid(492032), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 32, 31, 31), (30848, 961, 31, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 32, 31, 31), (30752, 961, 31, 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, 123008, grid=grid(123008), 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, 64, 29, 29), (53824, 841, 29, 1))
        buf13 = empty_strided_cuda((4, 32, 31, 31), (30848, 961, 31, 1), torch.int8)
        buf14 = empty_strided_cuda((4, 32, 31, 31), (30752, 961, 31, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_pool2d_2, x_2], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
        triton_poi_fused_max_pool2d_with_indices_relu_1.run(buf12, buf13, buf14, 123008, grid=grid(123008), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 64, 29, 29), (53824, 841, 29, 1))
        buf5 = buf4; del buf4  # reuse
        buf16 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, conv2d_3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf5, buf16, primals_5, 215296, grid=grid(215296), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1), torch.float32)
        buf26 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 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, buf26, 50176, grid=grid(50176), stream=stream0)
        buf8 = empty_strided_cuda((196, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf7, (196, 256), (256, 1), 0), reinterpret_tensor(primals_6, (256, 200), (1, 256), 0), out=buf8)
        buf17 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1), torch.int8)
        buf18 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1), torch.float32)
        buf25 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1), torch.bool)
        # Topologically Sorted Source Nodes: [max_pool2d_3, x_3], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.threshold_backward]
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3.run(buf16, buf17, buf18, buf25, 50176, grid=grid(50176), stream=stream0)
        buf19 = empty_strided_cuda((196, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf18, (196, 256), (256, 1), 0), reinterpret_tensor(primals_6, (256, 200), (1, 256), 0), out=buf19)
        buf9 = buf8; del buf8  # reuse
        buf20 = buf19; del buf19  # reuse
        # Topologically Sorted Source Nodes: [x1_1, x2_1], Original ATen: [aten.relu]
        triton_poi_fused_relu_4.run(buf9, buf20, primals_7, 39200, grid=grid(39200), stream=stream0)
        del primals_7
        buf10 = empty_strided_cuda((196, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x1_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf9, reinterpret_tensor(primals_8, (200, 10), (1, 200), 0), alpha=1, beta=1, out=buf10)
        buf21 = empty_strided_cuda((196, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x2_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf20, reinterpret_tensor(primals_8, (200, 10), (1, 200), 0), alpha=1, beta=1, out=buf21)
        del primals_9
        buf22 = empty_strided_cuda((196, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.cat, aten.relu]
        triton_poi_fused_cat_relu_5.run(buf10, buf21, buf22, 3920, grid=grid(3920), stream=stream0)
        buf23 = empty_strided_cuda((196, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf22, reinterpret_tensor(primals_11, (20, 2), (1, 20), 0), out=buf23)
        buf24 = buf23; del buf23  # reuse
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_6.run(buf24, primals_12, 392, grid=grid(392), stream=stream0)
        del primals_12
    return (buf24, buf10, buf21, primals_1, primals_3, primals_4, primals_10, buf1, buf2, buf3, buf5, buf6, reinterpret_tensor(buf7, (196, 256), (256, 1), 0), buf9, buf12, buf13, buf14, buf16, buf17, reinterpret_tensor(buf18, (196, 256), (256, 1), 0), buf20, buf22, buf24, primals_11, primals_8, primals_6, buf25, buf26, )


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, 1, 3, 3), (9, 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, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((200, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((200, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((10, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((2, 20), (20, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((2, ), (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])
    return print_performance(fn, times=times, repeat=repeat)


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

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


class AuxsiameseCNN(nn.Module):
    """
    basic structure similar to the CNN
    input is splited into two 1*14*14 images for separating training, share the same parameters
    softmax for the auxiliary output layers
    """

    def __init__(self):
        super(AuxsiameseCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(256, 200)
        self.fc2 = nn.Linear(200, 10)
        self.fc3 = nn.Linear(20, 2)

    def convs(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), kernel_size=2))
        x = F.relu(F.max_pool2d(self.conv2(x), kernel_size=2))
        return x

    def forward(self, x1, x2):
        x1 = self.convs(x1)
        x1 = x1.view(-1, 256)
        x1 = F.relu(self.fc1(x1))
        x1 = self.fc2(x1)
        x2 = self.convs(x2)
        x2 = x2.view(-1, 256)
        x2 = F.relu(self.fc1(x2))
        x2 = self.fc2(x2)
        x = torch.cat([x1, x2], dim=1)
        x = F.relu(x)
        x = torch.sigmoid(self.fc3(x))
        return x, x1, x2


def get_inputs():
    return [torch.rand([4, 1, 64, 64]), 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 import nn
from torch.nn import functional as F
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_out_ptr1, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 492032
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(in_out_ptr1 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 123008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 31
    x3 = xindex // 31
    x2 = xindex // 30752
    x4 = xindex % 30752
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 124 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 124 * x3), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (62 + 2 * x0 + 124 * x3), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (63 + 2 * x0 + 124 * 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 + 30848 * x2), tmp15, xmask)
    tl.store(out_ptr1 + x5, tmp18, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 215296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 841 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(in_out_ptr1 + x3, tmp4, 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 = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 14
    x1 = xindex // 14 % 14
    x2 = xindex // 196
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 58 * x1 + 841 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 58 * x1 + 841 * x2), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (29 + 2 * x0 + 58 * x1 + 841 * x2), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (30 + 2 * x0 + 58 * x1 + 841 * 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)
    tmp19 = 0.0
    tmp20 = tmp18 <= tmp19
    tl.store(out_ptr0 + x3, tmp15, xmask)
    tl.store(out_ptr1 + x3, tmp18, xmask)
    tl.store(out_ptr2 + x3, tmp20, xmask)


@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 39200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_cat_relu_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 3920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = xindex // 20
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 10, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (10 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 20, tl.int64)
    tmp9 = tl.load(in_ptr1 + (10 * x1 + (-10 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_sigmoid_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 392
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (32, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (200, 256), (256, 1))
    assert_size_stride(primals_7, (200,), (1,))
    assert_size_stride(primals_8, (10, 200), (200, 1))
    assert_size_stride(primals_9, (10,), (1,))
    assert_size_stride(primals_10, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_11, (2, 20), (20, 1))
    assert_size_stride(primals_12, (2,), (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, 62, 62), (123008, 3844, 62, 1))
        buf11 = extern_kernels.convolution(primals_10, primals_1, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 32, 62, 62), (123008, 3844, 62, 1))
        buf1 = buf0
        del buf0
        buf12 = buf11
        del buf11
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(492032)](buf1, buf12, primals_2,
            492032, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 32, 31, 31), (30848, 961, 31, 1),
            torch.int8)
        buf3 = empty_strided_cuda((4, 32, 31, 31), (30752, 961, 31, 1),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_1[grid(123008)](buf1,
            buf2, buf3, 123008, 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, 64, 29, 29), (53824, 841, 29, 1))
        buf13 = empty_strided_cuda((4, 32, 31, 31), (30848, 961, 31, 1),
            torch.int8)
        buf14 = empty_strided_cuda((4, 32, 31, 31), (30752, 961, 31, 1),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_1[grid(123008)](buf12,
            buf13, buf14, 123008, XBLOCK=512, num_warps=8, num_stages=1)
        buf15 = extern_kernels.convolution(buf14, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 64, 29, 29), (53824, 841, 29, 1))
        buf5 = buf4
        del buf4
        buf16 = buf15
        del buf15
        triton_poi_fused_convolution_2[grid(215296)](buf5, buf16, primals_5,
            215296, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1),
            torch.int8)
        buf7 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1),
            torch.float32)
        buf26 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1),
            torch.bool)
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3[grid
            (50176)](buf5, buf6, buf7, buf26, 50176, XBLOCK=512, num_warps=
            4, num_stages=1)
        buf8 = empty_strided_cuda((196, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (196, 256), (256, 1), 0),
            reinterpret_tensor(primals_6, (256, 200), (1, 256), 0), out=buf8)
        buf17 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1),
            torch.int8)
        buf18 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1),
            torch.float32)
        buf25 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1),
            torch.bool)
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3[grid
            (50176)](buf16, buf17, buf18, buf25, 50176, XBLOCK=512,
            num_warps=4, num_stages=1)
        buf19 = empty_strided_cuda((196, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf18, (196, 256), (256, 1), 0
            ), reinterpret_tensor(primals_6, (256, 200), (1, 256), 0), out=
            buf19)
        buf9 = buf8
        del buf8
        buf20 = buf19
        del buf19
        triton_poi_fused_relu_4[grid(39200)](buf9, buf20, primals_7, 39200,
            XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf10 = empty_strided_cuda((196, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf9, reinterpret_tensor(primals_8,
            (200, 10), (1, 200), 0), alpha=1, beta=1, out=buf10)
        buf21 = empty_strided_cuda((196, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf20, reinterpret_tensor(primals_8,
            (200, 10), (1, 200), 0), alpha=1, beta=1, out=buf21)
        del primals_9
        buf22 = empty_strided_cuda((196, 20), (20, 1), torch.float32)
        triton_poi_fused_cat_relu_5[grid(3920)](buf10, buf21, buf22, 3920,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf23 = empty_strided_cuda((196, 2), (2, 1), torch.float32)
        extern_kernels.mm(buf22, reinterpret_tensor(primals_11, (20, 2), (1,
            20), 0), out=buf23)
        buf24 = buf23
        del buf23
        triton_poi_fused_sigmoid_6[grid(392)](buf24, primals_12, 392,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_12
    return (buf24, buf10, buf21, primals_1, primals_3, primals_4,
        primals_10, buf1, buf2, buf3, buf5, buf6, reinterpret_tensor(buf7,
        (196, 256), (256, 1), 0), buf9, buf12, buf13, buf14, buf16, buf17,
        reinterpret_tensor(buf18, (196, 256), (256, 1), 0), buf20, buf22,
        buf24, primals_11, primals_8, primals_6, buf25, buf26)


class AuxsiameseCNNNew(nn.Module):
    """
    basic structure similar to the CNN
    input is splited into two 1*14*14 images for separating training, share the same parameters
    softmax for the auxiliary output layers
    """

    def __init__(self):
        super(AuxsiameseCNNNew, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(256, 200)
        self.fc2 = nn.Linear(200, 10)
        self.fc3 = nn.Linear(20, 2)

    def convs(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), kernel_size=2))
        x = F.relu(F.max_pool2d(self.conv2(x), kernel_size=2))
        return x

    def forward(self, input_0, input_1):
        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_11 = self.fc3.weight
        primals_12 = self.fc3.bias
        primals_3 = input_0
        primals_10 = input_1
        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])
        return output[0], output[1], output[2]
