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

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


# kernel path: runs/run_shard_7/inductor_cache/5q/c5qszywlnxbtnfe4vaxszwsgx5xl5pheqqp6qgfur2uarqqomlzp.py
# Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   input_1 => convolution
#   input_2 => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_0 = async_compile.triton('triton_poi_fused__prelu_kernel_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: '*fp32', 3: '*fp32', 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__prelu_kernel_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], '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__prelu_kernel_convolution_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 153760
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3844) % 10
    x0 = xindex % 3844
    x4 = (xindex // 3844)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x0 + (3872*x4)), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wk/cwk36g2szifjg2tg3e7hmeqah522icfg3cs7orxvzv3ewug2oqso.py
# Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_3 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 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_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_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 38440
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 31
    x1 = (xindex // 31) % 31
    x4 = (xindex // 961)
    x3 = (xindex // 9610)
    x5 = xindex % 9610
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (124*x1) + (3872*x4)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (124*x1) + (3872*x4)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (62 + (2*x0) + (124*x1) + (3872*x4)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (63 + (2*x0) + (124*x1) + (3872*x4)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x5 + (9632*x3)), tmp6, xmask)
    tl.store(out_ptr1 + (x5 + (9728*x3)), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/3m/c3msss2khn5e35u6efs7jvkbb4y2m2rijxwkj53esxai3amdl7po.py
# Topologically Sorted Source Nodes: [input_4, input_5], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   input_4 => convolution_1
#   input_5 => gt_1, mul_1, where_1
# Graph fragment:
#   %convolution_1 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_5, %primals_6, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %convolution_1), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_2 = async_compile.triton('triton_poi_fused__prelu_kernel_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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__prelu_kernel_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], '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__prelu_kernel_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 53824
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 841) % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ac/cac2zmdfv6mkn4vayrt5wv63uwtchzt5qdmsmwzfkgnbi5opnbsj.py
# Topologically Sorted Source Nodes: [input_6, input_7], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   input_6 => convolution_2
#   input_7 => gt_2, mul_2, where_2
# Graph fragment:
#   %convolution_2 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %convolution_2), kwargs = {})
#   %where_2 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_2), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_3 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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: '*fp32', 2: '*fp32', 3: '*fp32', 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__prelu_kernel_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], '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__prelu_kernel_convolution_3(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 93312
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 729) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2h/c2hr6qbw7g7f4tgu37vxblf2v52wndz43dcax7wyfspkvuizrkdp.py
# Topologically Sorted Source Nodes: [b], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   b => convolution_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_13, %primals_14, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_4 = async_compile.triton('triton_poi_fused_convolution_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=[16384], 
    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_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_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 11664
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 729) % 4
    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_7/inductor_cache/mw/cmwqc5igrl5va2uk477tyuqeq74fml4knprgxbdehp46p7gyy3vm.py
# Topologically Sorted Source Nodes: [a, a_1], Original ATen: [aten.convolution, aten._softmax]
# Source node to ATen node mapping:
#   a => convolution_3
#   a_1 => amax, exp, sub
# Graph fragment:
#   %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_11, %primals_12, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%convolution_3, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_3, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_convolution_5 = async_compile.triton('triton_poi_fused__softmax_convolution_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_convolution_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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__softmax_convolution_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 729) % 2
    x0 = xindex % 729
    x2 = (xindex // 1458)
    x4 = xindex % 1458
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (x0 + (1458*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (0))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp7 = tl.load(in_ptr0 + (729 + x0 + (1458*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp6 = tmp3 + tmp5
    tmp10 = tmp7 + tmp9
    tmp11 = triton_helpers.maximum(tmp6, tmp10)
    tmp12 = tmp2 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tl.store(out_ptr0 + (x4 + (1472*x2)), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fq/cfqhjy3rbf3vt6u7pq7upoxnnfcd2rx2kc7b7wbcggfe3idx7nx3.py
# Topologically Sorted Source Nodes: [a_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   a_1 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_6 = async_compile.triton('triton_poi_fused__softmax_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_6', 'mutated_arg_names': [], '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__softmax_6(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 1458)
    x3 = xindex % 1458
    x0 = xindex % 729
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (1472*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (1472*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (729 + x0 + (1472*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 / tmp3
    tl.store(out_ptr0 + (x4), tmp4, 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, primals_13, primals_14 = 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, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (10, ), (1, ))
    assert_size_stride(primals_5, (16, 10, 3, 3), (90, 9, 3, 1))
    assert_size_stride(primals_6, (16, ), (1, ))
    assert_size_stride(primals_7, (16, ), (1, ))
    assert_size_stride(primals_8, (32, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (32, ), (1, ))
    assert_size_stride(primals_10, (32, ), (1, ))
    assert_size_stride(primals_11, (2, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_12, (2, ), (1, ))
    assert_size_stride(primals_13, (4, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [input_1], 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, 62, 62), (38440, 3844, 62, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 10, 62, 62), (38720, 3872, 62, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten._prelu_kernel]
        stream0 = get_raw_stream(0)
        triton_poi_fused__prelu_kernel_convolution_0.run(buf1, primals_2, primals_4, buf2, 153760, grid=grid(153760), stream=stream0)
        del primals_2
        buf3 = empty_strided_cuda((4, 10, 31, 31), (9632, 961, 31, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 10, 31, 31), (9728, 961, 31, 1), torch.int8)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf2, buf3, buf4, 38440, grid=grid(38440), stream=stream0)
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf3, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 16, 29, 29), (13456, 841, 29, 1))
        buf6 = buf5; del buf5  # reuse
        buf7 = empty_strided_cuda((4, 16, 29, 29), (13456, 841, 29, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_4, input_5], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_2.run(buf6, primals_6, primals_7, buf7, 53824, grid=grid(53824), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 27, 27), (23328, 729, 27, 1))
        buf9 = buf8; del buf8  # reuse
        buf10 = empty_strided_cuda((4, 32, 27, 27), (23328, 729, 27, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_6, input_7], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_3.run(buf9, primals_9, primals_10, buf10, 93312, grid=grid(93312), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [a], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_11, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 2, 27, 27), (1458, 729, 27, 1))
        # Topologically Sorted Source Nodes: [b], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf10, primals_13, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 27, 27), (2916, 729, 27, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [b], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf13, primals_14, 11664, grid=grid(11664), stream=stream0)
        del primals_14
        buf14 = empty_strided_cuda((4, 2, 27, 27), (1472, 729, 27, 1), torch.float32)
        # Topologically Sorted Source Nodes: [a, a_1], Original ATen: [aten.convolution, aten._softmax]
        triton_poi_fused__softmax_convolution_5.run(buf11, primals_12, buf14, 5832, grid=grid(5832), stream=stream0)
        del primals_12
        buf15 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [a_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_6.run(buf14, buf15, 5832, grid=grid(5832), stream=stream0)
        del buf14
    return (buf13, buf15, primals_1, primals_3, primals_4, primals_5, primals_7, primals_8, primals_10, primals_11, primals_13, buf1, buf2, buf3, buf4, buf6, buf7, buf9, buf10, 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, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, 10, 3, 3), (90, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((32, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((2, 32, 1, 1), (32, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 32, 1, 1), (32, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14])
    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.functional as F
import torch.nn as nn
from collections import OrderedDict


class PNet(nn.Module):

    def __init__(self):
        super(PNet, self).__init__()
        self.features = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(3, 
            10, 3, 1)), ('prelu1', nn.PReLU(10)), ('pool1', nn.MaxPool2d(2,
            2, ceil_mode=True)), ('conv2', nn.Conv2d(10, 16, 3, 1)), (
            'prelu2', nn.PReLU(16)), ('conv3', nn.Conv2d(16, 32, 3, 1)), (
            'prelu3', nn.PReLU(32))]))
        self.conv4_1 = nn.Conv2d(32, 2, 1, 1)
        self.conv4_2 = nn.Conv2d(32, 4, 1, 1)

    def forward(self, x):
        """
        Arguments:
            x: a float tensor with shape [batch_size, 3, h, w].
        Returns:
            b: a float tensor with shape [batch_size, 4, h', w'].
            a: a float tensor with shape [batch_size, 2, h', w'].
        """
        x = self.features(x)
        a = self.conv4_1(x)
        b = self.conv4_2(x)
        a = F.softmax(a)
        return b, a


def get_inputs():
    return [torch.rand([4, 3, 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 torch.nn as nn
from collections import OrderedDict
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__prelu_kernel_convolution_0(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 153760
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 10
    x0 = xindex % 3844
    x4 = xindex // 3844
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + (x0 + 3872 * x4), tmp7, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 38440
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 31
    x1 = xindex // 31 % 31
    x4 = xindex // 961
    x3 = xindex // 9610
    x5 = xindex % 9610
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (62 + 2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (63 + 2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x5 + 9632 * x3), tmp6, xmask)
    tl.store(out_ptr1 + (x5 + 9728 * x3), tmp16, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_2(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 53824
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 841 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_3(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 93312
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 729 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 11664
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 729 % 4
    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__softmax_convolution_5(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 5832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 729 % 2
    x0 = xindex % 729
    x2 = xindex // 1458
    x4 = xindex % 1458
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (x0 + 1458 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr1 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp7 = tl.load(in_ptr0 + (729 + x0 + 1458 * x2), xmask, eviction_policy
        ='evict_last')
    tmp8 = tl.load(in_ptr1 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp6 = tmp3 + tmp5
    tmp10 = tmp7 + tmp9
    tmp11 = triton_helpers.maximum(tmp6, tmp10)
    tmp12 = tmp2 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tl.store(out_ptr0 + (x4 + 1472 * x2), tmp13, xmask)


@triton.jit
def triton_poi_fused__softmax_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 5832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 1458
    x3 = xindex % 1458
    x0 = xindex % 729
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 1472 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 1472 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (729 + x0 + 1472 * x2), xmask, eviction_policy
        ='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 / tmp3
    tl.store(out_ptr0 + x4, tmp4, 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,
        primals_13, primals_14) = 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, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (10,), (1,))
    assert_size_stride(primals_5, (16, 10, 3, 3), (90, 9, 3, 1))
    assert_size_stride(primals_6, (16,), (1,))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (32, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (32,), (1,))
    assert_size_stride(primals_11, (2, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_12, (2,), (1,))
    assert_size_stride(primals_13, (4, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_14, (4,), (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, 62, 62), (38440, 3844, 62, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 10, 62, 62), (38720, 3872, 62, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused__prelu_kernel_convolution_0[grid(153760)](buf1,
            primals_2, primals_4, buf2, 153760, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 10, 31, 31), (9632, 961, 31, 1),
            torch.float32)
        buf4 = empty_strided_cuda((4, 10, 31, 31), (9728, 961, 31, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(38440)](buf2, buf3,
            buf4, 38440, XBLOCK=512, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf3, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 16, 29, 29), (13456, 841, 29, 1))
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 16, 29, 29), (13456, 841, 29, 1),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_2[grid(53824)](buf6,
            primals_6, primals_7, buf7, 53824, XBLOCK=512, num_warps=4,
            num_stages=1)
        del primals_6
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 27, 27), (23328, 729, 27, 1))
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((4, 32, 27, 27), (23328, 729, 27, 1),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_3[grid(93312)](buf9,
            primals_9, primals_10, buf10, 93312, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_9
        buf11 = extern_kernels.convolution(buf10, primals_11, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 2, 27, 27), (1458, 729, 27, 1))
        buf12 = extern_kernels.convolution(buf10, primals_13, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 27, 27), (2916, 729, 27, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_4[grid(11664)](buf13, primals_14, 
            11664, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_14
        buf14 = empty_strided_cuda((4, 2, 27, 27), (1472, 729, 27, 1),
            torch.float32)
        triton_poi_fused__softmax_convolution_5[grid(5832)](buf11,
            primals_12, buf14, 5832, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_12
        buf15 = buf11
        del buf11
        triton_poi_fused__softmax_6[grid(5832)](buf14, buf15, 5832, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf14
    return (buf13, buf15, primals_1, primals_3, primals_4, primals_5,
        primals_7, primals_8, primals_10, primals_11, primals_13, buf1,
        buf2, buf3, buf4, buf6, buf7, buf9, buf10, buf15)


class PNetNew(nn.Module):

    def __init__(self):
        super(PNetNew, self).__init__()
        self.features = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(3, 
            10, 3, 1)), ('prelu1', nn.PReLU(10)), ('pool1', nn.MaxPool2d(2,
            2, ceil_mode=True)), ('conv2', nn.Conv2d(10, 16, 3, 1)), (
            'prelu2', nn.PReLU(16)), ('conv3', nn.Conv2d(16, 32, 3, 1)), (
            'prelu3', nn.PReLU(32))]))
        self.conv4_1 = nn.Conv2d(32, 2, 1, 1)
        self.conv4_2 = nn.Conv2d(32, 4, 1, 1)

    def forward(self, input_0):
        primals_1 = self.features.conv1.weight
        primals_2 = self.features.conv1.bias
        primals_4 = self.features.prelu1.weight
        primals_5 = self.features.conv2.weight
        primals_6 = self.features.conv2.bias
        primals_7 = self.features.prelu2.weight
        primals_8 = self.features.conv3.weight
        primals_9 = self.features.conv3.bias
        primals_10 = self.features.prelu3.weight
        primals_11 = self.conv4_1.weight
        primals_12 = self.conv4_1.bias
        primals_13 = self.conv4_2.weight
        primals_14 = self.conv4_2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0], output[1]
