# 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/jo/cjofd5z4dxar3hrs2mtbo6ielcpo5qfoz5e7szgk5223qqyf4wfy.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[2048, 64], tile_hint=TileHint.SQUARE,
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1600
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 20
    y1 = (yindex // 20)
    tmp0 = tl.load(in_ptr0 + (x2 + (64*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (20*x2) + (1280*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/et/cetk4nv4xhiobqu367vpvtmoqpch77aw3pjimoicgzr6pb3hnuhi.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_1 = async_compile.triton('triton_poi_fused_convolution_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=[128, 4096], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', '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_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 80
    xnumel = 3249
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 20
    y1 = (yindex // 20)
    tmp0 = tl.load(in_ptr0 + (x2 + (3249*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (y0 + (20*x2) + (64980*y1)), tmp2, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/jz/cjz2kuudiauwo7xsgvjrtx66xilfjlrngfx2mc7i5brazzi7rkbu.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, [1, 1], [1, 1], [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_2 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_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: '*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_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_2(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 259920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int8)
    tmp2 = tl.full([1], 0, tl.int32)
    tmp3 = triton_helpers.maximum(tmp2, tmp0)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
    tl.store(out_ptr1 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/lz/clzsziwmggw3irwt7rniv5j34ozeegxom3ittdyk3itcavxvkjyl.py
# Topologically Sorted Source Nodes: [conv2d_1, max_pool2d_1], Original ATen: [aten.convolution, aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   max_pool2d_1 => getitem_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 = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_convolution_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_convolution_max_pool2d_with_indices_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=[1048576], 
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_max_pool2d_with_indices_3', '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_max_pool2d_with_indices_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 800000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 80
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int8)
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/tl/ctlwfikzjqj2tzfxsbahkbkyzhmgtiswa62f2wsdrgl4clqcmgan.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
#   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, [1, 1], [1, 1], [0, 0], [1, 1], False), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_2,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[512, 4096], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_4(in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 320
    xnumel = 2500
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 80
    y1 = (yindex // 80)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (80*x2) + (200000*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + (x2 + (2528*y3)), tmp2, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (80*x2) + (200000*y1)), tmp4, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/cl/cclx7eadx3isekm5oexkytozpryp2h5ttbwir6frhvtzv346kjbu.py
# Topologically Sorted Source Nodes: [max_pool2d_1, x_1, x_2], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.view]
# Source node to ATen node mapping:
#   max_pool2d_1 => _low_memory_max_pool2d_with_offsets_1
#   x_1 => relu_1
#   x_2 => view
# 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, [1, 1], [1, 1], [0, 0], [1, 1], False), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_2,), kwargs = {})
#   %view : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%relu_1, [4, -1]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_view_5 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_view_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_relu_view_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_view_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 800000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 200000
    x1 = (xindex // 200000)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2528*(x0 // 2500)) + (202240*x1) + (x0 % 2500)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/vt/cvt6synwfekvpysrm7reg7gv4rzuxzdlida6fy4emfeue3qp6o2b.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_3 => relu_2
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_7), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_6 = async_compile.triton('triton_poi_fused_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_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_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 500
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/wa/cwavavqjyrtrr2nei6l6gsooekwutdp24qjvue3t265bbk3lgpr7.py
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_6 => relu_3
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_9), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_7 = async_compile.triton('triton_poi_fused_relu_threshold_backward_7', '''
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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_threshold_backward_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_7(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (20, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_2, (20, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (80, 20, 8, 8), (1280, 64, 8, 1))
    assert_size_stride(primals_5, (80, ), (1, ))
    assert_size_stride(primals_6, (500, 200000), (200000, 1))
    assert_size_stride(primals_7, (500, ), (1, ))
    assert_size_stride(primals_8, (8, 500), (500, 1))
    assert_size_stride(primals_9, (8, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((80, 20, 8, 8), (1280, 1, 160, 20), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_4, buf0, 1600, 64, grid=grid(1600, 64), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf1 = 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(buf1, (4, 20, 57, 57), (64980, 3249, 57, 1))
        buf2 = empty_strided_cuda((4, 20, 57, 57), (64980, 1, 1140, 20), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf1, primals_2, buf2, 80, 3249, grid=grid(80, 3249), stream=stream0)
        del primals_2
        buf3 = empty_strided_cuda((4, 20, 57, 57), (64980, 1, 1140, 20), torch.int8)
        buf4 = reinterpret_tensor(buf1, (4, 20, 57, 57), (64980, 1, 1140, 20), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [max_pool2d, x], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
        triton_poi_fused_max_pool2d_with_indices_relu_2.run(buf2, buf3, buf4, 259920, grid=grid(259920), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, buf0, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 80, 50, 50), (200000, 1, 4000, 80))
        buf6 = buf5; del buf5  # reuse
        buf7 = empty_strided_cuda((4, 80, 50, 50), (200000, 1, 4000, 80), torch.int8)
        # Topologically Sorted Source Nodes: [conv2d_1, max_pool2d_1], Original ATen: [aten.convolution, aten.max_pool2d_with_indices]
        triton_poi_fused_convolution_max_pool2d_with_indices_3.run(buf6, primals_5, buf7, 800000, grid=grid(800000), stream=stream0)
        del primals_5
        buf8 = empty_strided_cuda((4, 80, 50, 50), (202240, 2528, 50, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 80, 50, 50), (200000, 1, 4000, 80), 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_4.run(buf6, buf8, buf15, 320, 2500, grid=grid(320, 2500), stream=stream0)
        buf9 = empty_strided_cuda((4, 200000), (200000, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_pool2d_1, x_1, x_2], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.view]
        triton_poi_fused_max_pool2d_with_indices_relu_view_5.run(buf8, buf9, 800000, grid=grid(800000), stream=stream0)
        del buf8
        buf10 = empty_strided_cuda((4, 500), (500, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf9, reinterpret_tensor(primals_6, (200000, 500), (1, 200000), 0), out=buf10)
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf11, primals_7, 2000, grid=grid(2000), stream=stream0)
        del primals_7
        buf12 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf11, reinterpret_tensor(primals_8, (500, 8), (1, 500), 0), out=buf12)
        buf13 = buf12; del buf12  # reuse
        buf14 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_7.run(buf13, primals_9, buf14, 32, grid=grid(32), stream=stream0)
        del primals_9
    return (buf13, primals_1, primals_3, buf0, buf2, buf3, buf4, buf6, buf7, buf9, buf11, buf14, primals_8, primals_6, buf15, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((20, 1, 8, 8), (64, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((20, ), (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((80, 20, 8, 8), (1280, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((80, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((500, 200000), (200000, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((500, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((8, 500), (500, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


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

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


class Evidential_layer(nn.Module):

    def __init__(self, in_dim, num_classes):
        super(Evidential_layer, self).__init__()
        self.num_classes = num_classes
        self.fc1 = nn.Linear(in_dim, 2 * self.num_classes)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.fc1(x)
        return self.relu(x)


class MNISTmodel(nn.Module):

    def __init__(self, num_classes, edl, dropout=True):
        super(MNISTmodel, self).__init__()
        self.use_dropout = dropout
        k, m = 8, 80
        km = (64 - 2 * (k - 1)) ** 2 * m
        self.num_classes = num_classes
        self.conv1 = nn.Conv2d(1, 20, kernel_size=k)
        self.conv2 = nn.Conv2d(20, m, kernel_size=k)
        self.fc1 = nn.Linear(km, 500)
        if edl:
            self.fc2 = Evidential_layer(500, self.num_classes)
        else:
            self.fc2 = nn.Linear(500, self.num_classes)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 1))
        x = F.relu(F.max_pool2d(self.conv2(x), 1))
        x = x.view(x.size()[0], -1)
        x = F.relu(self.fc1(x))
        if self.use_dropout:
            x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return x


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


def get_init_inputs():
    return [[], {'num_classes': 4, 'edl': 4}]

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


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 1600
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 20
    y1 = yindex // 20
    tmp0 = tl.load(in_ptr0 + (x2 + 64 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 20 * x2 + 1280 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 80
    xnumel = 3249
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 20
    y1 = yindex // 20
    tmp0 = tl.load(in_ptr0 + (x2 + 3249 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (y0 + 20 * x2 + 64980 * y1), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_2(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 259920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int8)
    tmp2 = tl.full([1], 0, tl.int32)
    tmp3 = triton_helpers.maximum(tmp2, tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)
    tl.store(out_ptr1 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_max_pool2d_with_indices_3(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 800000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 80
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int8)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_4(in_ptr0,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 320
    xnumel = 2500
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 80
    y1 = yindex // 80
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 80 * x2 + 200000 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + (x2 + 2528 * y3), tmp2, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 80 * x2 + 200000 * y1), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_view_5(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 800000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 200000
    x1 = xindex // 200000
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2528 * (x0 // 2500) + 202240 * x1 + x0 % 2500
        ), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 2000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 500
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_7(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (20, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_2, (20,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (80, 20, 8, 8), (1280, 64, 8, 1))
    assert_size_stride(primals_5, (80,), (1,))
    assert_size_stride(primals_6, (500, 200000), (200000, 1))
    assert_size_stride(primals_7, (500,), (1,))
    assert_size_stride(primals_8, (8, 500), (500, 1))
    assert_size_stride(primals_9, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((80, 20, 8, 8), (1280, 1, 160, 20), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(1600, 64)](primals_4, buf0, 1600, 64,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf1 = 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(buf1, (4, 20, 57, 57), (64980, 3249, 57, 1))
        buf2 = empty_strided_cuda((4, 20, 57, 57), (64980, 1, 1140, 20),
            torch.float32)
        triton_poi_fused_convolution_1[grid(80, 3249)](buf1, primals_2,
            buf2, 80, 3249, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 20, 57, 57), (64980, 1, 1140, 20),
            torch.int8)
        buf4 = reinterpret_tensor(buf1, (4, 20, 57, 57), (64980, 1, 1140, 
            20), 0)
        del buf1
        triton_poi_fused_max_pool2d_with_indices_relu_2[grid(259920)](buf2,
            buf3, buf4, 259920, XBLOCK=1024, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 80, 50, 50), (200000, 1, 4000, 80))
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 80, 50, 50), (200000, 1, 4000, 80),
            torch.int8)
        triton_poi_fused_convolution_max_pool2d_with_indices_3[grid(800000)](
            buf6, primals_5, buf7, 800000, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_5
        buf8 = empty_strided_cuda((4, 80, 50, 50), (202240, 2528, 50, 1),
            torch.float32)
        buf15 = empty_strided_cuda((4, 80, 50, 50), (200000, 1, 4000, 80),
            torch.bool)
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_4[grid
            (320, 2500)](buf6, buf8, buf15, 320, 2500, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 200000), (200000, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_view_5[grid(800000)](buf8
            , buf9, 800000, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf8
        buf10 = empty_strided_cuda((4, 500), (500, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_6, (200000, 500),
            (1, 200000), 0), out=buf10)
        buf11 = buf10
        del buf10
        triton_poi_fused_relu_6[grid(2000)](buf11, primals_7, 2000, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_7
        buf12 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf11, reinterpret_tensor(primals_8, (500, 8), (1,
            500), 0), out=buf12)
        buf13 = buf12
        del buf12
        buf14 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_7[grid(32)](buf13,
            primals_9, buf14, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_9
    return (buf13, primals_1, primals_3, buf0, buf2, buf3, buf4, buf6, buf7,
        buf9, buf11, buf14, primals_8, primals_6, buf15)


class Evidential_layer(nn.Module):

    def __init__(self, in_dim, num_classes):
        super(Evidential_layer, self).__init__()
        self.num_classes = num_classes
        self.fc1 = nn.Linear(in_dim, 2 * self.num_classes)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.fc1(x)
        return self.relu(x)


class MNISTmodelNew(nn.Module):

    def __init__(self, num_classes, edl, dropout=True):
        super(MNISTmodelNew, self).__init__()
        self.use_dropout = dropout
        k, m = 8, 80
        km = (64 - 2 * (k - 1)) ** 2 * m
        self.num_classes = num_classes
        self.conv1 = nn.Conv2d(1, 20, kernel_size=k)
        self.conv2 = nn.Conv2d(20, m, kernel_size=k)
        self.fc1 = nn.Linear(km, 500)
        if edl:
            self.fc2 = Evidential_layer(500, self.num_classes)
        else:
            self.fc2 = nn.Linear(500, self.num_classes)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.fc1.weight
        primals_9 = self.fc2.fc1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
