# 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/dj/cdjtpyt5zghcvffzxa6wkjmodj3o7syavliioml7niive7obzd4e.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=[65536, 128], 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), 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 = 65536
    xnumel = 81
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (81*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (20736*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ge/cge7bnkkv3lovy5jtwqk5sazg4aouf273qc7u6jafiuctxmosb55.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_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=[1024, 4096], 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_1', '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_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1024
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), None, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (1048576*y1)), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/r6/cr6ia57wuoco53lexvas2s6mswc63dlghrk4z5v2yjt6qpkp6y5y.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 802816
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yv/cyv75ix3abbpjnp4kuxktubs5khwx735tm64lqxzehoe3xz4cgsy.py
# Topologically Sorted Source Nodes: [pow_1, squared_norm], Original ATen: [aten.pow, aten.sum]
# Source node to ATen node mapping:
#   pow_1 => pow_1
#   squared_norm => sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_1, 2), kwargs = {})
#   %sum_1 : [num_users=4] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
triton_red_fused_pow_sum_3 = async_compile.triton('triton_red_fused_pow_sum_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.reduction(
    size_hints=[8192, 128],
    reduction_hint=ReductionHint.OUTER,
    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_red_fused_pow_sum_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused_pow_sum_3(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 6272
    rnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 8
    x1 = (xindex // 8) % 196
    x2 = (xindex // 1568)
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp0 = tl.load(in_ptr0 + ((32*x0) + (256*((r3 + (128*x1)) % 784)) + (200704*x2) + ((r3 + (128*x1)) // 784)), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask & xmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tl.store(out_ptr0 + (x4), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4d/c4ddwhtzgzsmxbeu7xngnhm32yxegk443x2wgjyupklf36lrrckw.py
# Topologically Sorted Source Nodes: [pow_1, squared_norm], Original ATen: [aten.pow, aten.sum]
# Source node to ATen node mapping:
#   pow_1 => pow_1
#   squared_norm => sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_1, 2), kwargs = {})
#   %sum_1 : [num_users=4] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
triton_red_fused_pow_sum_4 = async_compile.triton('triton_red_fused_pow_sum_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.reduction(
    size_hints=[32, 256],
    reduction_hint=ReductionHint.OUTER_TINY,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_pow_sum_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused_pow_sum_4(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 32
    rnumel = 196
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 8
    x1 = (xindex // 8)
    _tmp2 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (x0 + (8*r2) + (1568*x1)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp3 = _tmp2 + tmp1
        _tmp2 = tl.where(rmask & xmask, tmp3, _tmp2)
    tmp2 = tl.sum(_tmp2, 1)[:, None]
    tl.store(out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/qv/cqvdqjqmaz7ljgkjsc6bws625p5iycl6ax3p5cznxw3ydqrjge63.py
# Topologically Sorted Source Nodes: [mul, add, sqrt, mul_1, output_tensor], Original ATen: [aten.mul, aten.add, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   mul_1 => mul_1
#   output_tensor => div
#   sqrt => sqrt
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, %view_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1.0), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %sqrt), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_div_mul_sqrt_5 = async_compile.triton('triton_poi_fused_add_div_mul_sqrt_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: '*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_add_div_mul_sqrt_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_add_div_mul_sqrt_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 802816
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = (xindex // 25088)
    x0 = xindex % 25088
    x1 = (xindex // 25088) % 8
    x2 = (xindex // 200704)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((32*x1) + (256*(x0 % 784)) + (200704*x2) + (x0 // 784)), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp0 + tmp3
    tmp5 = libdevice.sqrt(tmp0)
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 / tmp6
    tl.store(out_ptr0 + (x4), tmp7, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (256, 256, 9, 9), (20736, 81, 9, 1))
    assert_size_stride(primals_2, (256, ), (1, ))
    assert_size_stride(primals_3, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 256, 9, 9), (20736, 1, 2304, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 65536, 81, grid=grid(65536, 81), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_3, buf1, 1024, 4096, grid=grid(1024, 4096), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 256, 28, 28), (200704, 1, 7168, 256))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf3, primals_2, 802816, grid=grid(802816), stream=stream0)
        del primals_2
        buf4 = empty_strided_cuda((4, 8, 1, 196), (1568, 1, 6272, 8), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, squared_norm], Original ATen: [aten.pow, aten.sum]
        triton_red_fused_pow_sum_3.run(buf3, buf4, 6272, 128, grid=grid(6272), stream=stream0)
        buf5 = empty_strided_cuda((4, 8, 1), (8, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, squared_norm], Original ATen: [aten.pow, aten.sum]
        triton_red_fused_pow_sum_4.run(buf4, buf5, 32, 196, grid=grid(32), stream=stream0)
        del buf4
        buf6 = empty_strided_cuda((4, 8, 25088), (200704, 25088, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, add, sqrt, mul_1, output_tensor], Original ATen: [aten.mul, aten.add, aten.sqrt, aten.div]
        triton_poi_fused_add_div_mul_sqrt_5.run(buf5, buf3, buf6, 802816, grid=grid(802816), stream=stream0)
    return (reinterpret_tensor(buf6, (4, 25088, 8), (200704, 1, 25088), 0), buf0, buf1, buf3, buf5, )


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((256, 256, 9, 9), (20736, 81, 9, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn


class PrimaryCaps(nn.Module):
    """
        输入:(B,C,H,W)=(B,256,20,20)
        输出：(B,C_N,C_L)=(B,32*6*6, 8)=(B,1152,8)
        C_N:capsule_num，胶囊的个数
        C_L:capsule_length，每个胶囊的长度
    """

    def __init__(self, capsule_length=8, in_channels=256, out_channels=32,
        capsule_num=32 * 6 * 6, kernel_size=9, stride=2, padding=0):
        super(PrimaryCaps, self).__init__()
        self.capsule_length = capsule_length
        self.capsule_num = capsule_num
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels * capsule_length, kernel_size=kernel_size, stride=
            stride, padding=padding)

    def forward(self, x):
        """
        :param x: (B,C,H,W) -> (B,256,20,20)
        :return: (B,C_N,C_L) -> (100,32*6*6,8) = (100,1152,8)
        """
        x = self.conv(x)
        x = self.toCapsules(x)
        return x

    def toCapsules(self, x):
        B = x.size(0)
        x.size(1)
        H = x.size(2)
        W = x.size(3)
        x = x.reshape(B, self.capsule_length, -1, H, W)
        x = x.reshape(B, self.capsule_length, -1)
        x = self.squash(x)
        x = x.permute(0, 2, 1)
        return x

    def squash(self, input_tensor):
        """
        input_tensor: (B, 1, 10, 16)
        return: output_tensor: (B, 1, 10, 16)
        """
        squared_norm = (input_tensor ** 2).sum(-1, keepdim=True)
        output_tensor = squared_norm * input_tensor / ((1.0 + squared_norm) *
            torch.sqrt(squared_norm))
        return output_tensor


def get_inputs():
    return [torch.rand([4, 256, 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.triton_helpers import libdevice
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):
    xnumel = 81
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 81 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 256 * x2 + 20736 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 256 * x2 + 1048576 * y1), tmp0, None)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_red_fused_pow_sum_3(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 6272
    rnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 8
    x1 = xindex // 8 % 196
    x2 = xindex // 1568
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp0 = tl.load(in_ptr0 + (32 * x0 + 256 * ((r3 + 128 * x1) % 784) +
            200704 * x2 + (r3 + 128 * x1) // 784), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask & xmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tl.store(out_ptr0 + x4, tmp3, xmask)


@triton.jit
def triton_red_fused_pow_sum_4(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 32
    rnumel = 196
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 8
    x1 = xindex // 8
    _tmp2 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (x0 + 8 * r2 + 1568 * x1), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp3 = _tmp2 + tmp1
        _tmp2 = tl.where(rmask & xmask, tmp3, _tmp2)
    tmp2 = tl.sum(_tmp2, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sqrt_5(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex // 25088
    x0 = xindex % 25088
    x1 = xindex // 25088 % 8
    x2 = xindex // 200704
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (32 * x1 + 256 * (x0 % 784) + 200704 * x2 + x0 //
        784), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp0 + tmp3
    tmp5 = libdevice.sqrt(tmp0)
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 / tmp6
    tl.store(out_ptr0 + x4, tmp7, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (256, 256, 9, 9), (20736, 81, 9, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 256, 9, 9), (20736, 1, 2304, 256),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(65536, 81)](primals_1, buf0, 65536, 81,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        triton_poi_fused_1[grid(1024, 4096)](primals_3, buf1, 1024, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 256, 28, 28), (200704, 1, 7168, 256))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_2[grid(802816)](buf3, primals_2, 
            802816, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf4 = empty_strided_cuda((4, 8, 1, 196), (1568, 1, 6272, 8), torch
            .float32)
        triton_red_fused_pow_sum_3[grid(6272)](buf3, buf4, 6272, 128,
            XBLOCK=64, RBLOCK=8, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 8, 1), (8, 1, 1), torch.float32)
        triton_red_fused_pow_sum_4[grid(32)](buf4, buf5, 32, 196, XBLOCK=2,
            RBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        buf6 = empty_strided_cuda((4, 8, 25088), (200704, 25088, 1), torch.
            float32)
        triton_poi_fused_add_div_mul_sqrt_5[grid(802816)](buf5, buf3, buf6,
            802816, XBLOCK=1024, num_warps=4, num_stages=1)
    return reinterpret_tensor(buf6, (4, 25088, 8), (200704, 1, 25088), 0
        ), buf0, buf1, buf3, buf5


class PrimaryCapsNew(nn.Module):
    """
        输入:(B,C,H,W)=(B,256,20,20)
        输出：(B,C_N,C_L)=(B,32*6*6, 8)=(B,1152,8)
        C_N:capsule_num，胶囊的个数
        C_L:capsule_length，每个胶囊的长度
    """

    def __init__(self, capsule_length=8, in_channels=256, out_channels=32,
        capsule_num=32 * 6 * 6, kernel_size=9, stride=2, padding=0):
        super(PrimaryCapsNew, self).__init__()
        self.capsule_length = capsule_length
        self.capsule_num = capsule_num
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels * capsule_length, kernel_size=kernel_size, stride=
            stride, padding=padding)

    def toCapsules(self, x):
        B = x.size(0)
        x.size(1)
        H = x.size(2)
        W = x.size(3)
        x = x.reshape(B, self.capsule_length, -1, H, W)
        x = x.reshape(B, self.capsule_length, -1)
        x = self.squash(x)
        x = x.permute(0, 2, 1)
        return x

    def squash(self, input_tensor):
        """
        input_tensor: (B, 1, 10, 16)
        return: output_tensor: (B, 1, 10, 16)
        """
        squared_norm = (input_tensor ** 2).sum(-1, keepdim=True)
        output_tensor = squared_norm * input_tensor / ((1.0 + squared_norm) *
            torch.sqrt(squared_norm))
        return output_tensor

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