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

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


# kernel path: runs/run_shard_4/inductor_cache/pr/cprzmk46lzbq3wwm3fdlxhtbnwymkr6wunytnriiqf3dz27ndnvh.py
# Topologically Sorted Source Nodes: [sub, relu, left, mul, sub_2, mul_1, sub_1, relu_1, right, mul_2, margin_loss, margin_loss_1], Original ATen: [aten.rsub, aten.relu, aten.pow, aten.mul, aten.sub, aten.add, aten.sum]
# Source node to ATen node mapping:
#   left => pow_1
#   margin_loss => add
#   margin_loss_1 => sum_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   relu => relu
#   relu_1 => relu_1
#   right => pow_2
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (0.9, %arg1_1), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%relu, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, %pow_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg2_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, 0.5), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, 0.1), kwargs = {})
#   %relu_1 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_1,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%relu_1, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %pow_2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%add,), kwargs = {})
triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_add_mul_pow_relu_rsub_sub_sum_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp2 = 0.9
    tmp3 = tmp2 - tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp0 * tmp6
    tmp8 = 1.0
    tmp9 = tmp8 - tmp0
    tmp10 = 0.5
    tmp11 = tmp9 * tmp10
    tmp12 = 0.1
    tmp13 = tmp1 - tmp12
    tmp14 = triton_helpers.maximum(tmp4, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp11 * tmp15
    tmp17 = tmp7 + tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp20, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2i/c2iudvf7nvwf6e57k65u5c7ley2r4yshxdnqzbds6k36qn535ehv.py
# Topologically Sorted Source Nodes: [sub_3, pow_3, recons_loss], Original ATen: [aten.sub, aten.pow, aten.sum]
# Source node to ATen node mapping:
#   pow_3 => pow_3
#   recons_loss => sum_2
#   sub_3 => sub_3
# Graph fragment:
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %arg3_1), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_3,), kwargs = {})
triton_red_fused_pow_sub_sum_1 = async_compile.triton('triton_red_fused_pow_sub_sum_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.reduction(
    size_hints=[8, 8192],
    reduction_hint=ReductionHint.INNER,
    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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_pow_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 8
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp5 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 % 4096), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + (r1 + (8192*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 - tmp1
        tmp3 = tmp2 * tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp6 = _tmp5 + tmp4
        _tmp5 = tl.where(rmask & xmask, tmp6, _tmp5)
    tmp5 = tl.sum(_tmp5, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tw/ctw54bhhmc22e2rmlozzpqh6hw65h3wqmyh357ii2bgalesstcsq.py
# Topologically Sorted Source Nodes: [sub_3, pow_3, recons_loss, mul_3, add_1, truediv], Original ATen: [aten.sub, aten.pow, aten.sum, aten.mul, aten.add, aten.div]
# Source node to ATen node mapping:
#   add_1 => add_1
#   mul_3 => mul_3
#   pow_3 => pow_3
#   recons_loss => sum_2
#   sub_3 => sub_3
#   truediv => div
# Graph fragment:
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %arg3_1), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_3,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 0.0005), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %mul_3), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, 4), kwargs = {})
triton_per_fused_add_div_mul_pow_sub_sum_2 = async_compile.triton('triton_per_fused_add_div_mul_pow_sub_sum_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.persistent_reduction(
    size_hints=[1, 8],
    reduction_hint=ReductionHint.INNER,
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mul_pow_sub_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_mul_pow_sub_sum_2(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 8
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp4 = tl.load(in_out_ptr0 + (0))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, 1])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp6 = 0.0005
    tmp7 = tmp3 * tmp6
    tmp8 = tmp5 + tmp7
    tmp9 = 0.25
    tmp10 = tmp8 * tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp10, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 64), (1024, 256, 64, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 1024), (16384, 4096, 1024, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [sub, relu, left, mul, sub_2, mul_1, sub_1, relu_1, right, mul_2, margin_loss, margin_loss_1], Original ATen: [aten.rsub, aten.relu, aten.pow, aten.mul, aten.sub, aten.add, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0.run(arg2_1, arg1_1, buf0, 1, 256, grid=grid(1), stream=stream0)
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((8, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [sub_3, pow_3, recons_loss], Original ATen: [aten.sub, aten.pow, aten.sum]
        triton_red_fused_pow_sub_sum_1.run(arg0_1, arg3_1, buf1, 8, 8192, grid=grid(8), stream=stream0)
        del arg0_1
        del arg3_1
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [sub_3, pow_3, recons_loss, mul_3, add_1, truediv], Original ATen: [aten.sub, aten.pow, aten.sum, aten.mul, aten.add, aten.div]
        triton_per_fused_add_div_mul_pow_sub_sum_2.run(buf3, buf1, 1, 8, grid=grid(1), stream=stream0)
        del buf1
    return (buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 64), (1024, 256, 64, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4, 1024), (16384, 4096, 1024, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1])
    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
import torch.nn.functional as F


class CapsuleLoss(nn.Module):

    def __init__(self):
        super(CapsuleLoss, self).__init__()

    def forward(self, inputs, labels, logits, recons):
        batch_size = inputs.shape[0]
        left = F.relu(0.9 - logits, inplace=True) ** 2
        right = F.relu(logits - 0.1, inplace=True) ** 2
        margin_loss = labels * left + 0.5 * (1.0 - labels) * right
        margin_loss = margin_loss.sum()
        recons_loss = ((inputs.view(batch_size, -1) - recons) ** 2).sum()
        return (margin_loss + 0.0005 * recons_loss) / batch_size


def get_inputs():
    return [torch.rand([4, 4, 4, 64]), torch.rand([4, 4, 4, 4]), torch.rand
        ([4, 4, 4, 4]), torch.rand([4, 4, 4, 1024])]


def get_init_inputs():
    return [[], {}]

import torch
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


@triton.jit
def triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0(in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = 0.9
    tmp3 = tmp2 - tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp0 * tmp6
    tmp8 = 1.0
    tmp9 = tmp8 - tmp0
    tmp10 = 0.5
    tmp11 = tmp9 * tmp10
    tmp12 = 0.1
    tmp13 = tmp1 - tmp12
    tmp14 = triton_helpers.maximum(tmp4, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp11 * tmp15
    tmp17 = tmp7 + tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp20, None)


@triton.jit
def triton_red_fused_pow_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 8
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp5 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + r1 % 4096, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + (r1 + 8192 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 - tmp1
        tmp3 = tmp2 * tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp6 = _tmp5 + tmp4
        _tmp5 = tl.where(rmask & xmask, tmp6, _tmp5)
    tmp5 = tl.sum(_tmp5, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_per_fused_add_div_mul_pow_sub_sum_2(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp4 = tl.load(in_out_ptr0 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, 1])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp6 = 0.0005
    tmp7 = tmp3 * tmp6
    tmp8 = tmp5 + tmp7
    tmp9 = 0.25
    tmp10 = tmp8 * tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp10, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 64), (1024, 256, 64, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 1024), (16384, 4096, 1024, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0[grid(1)](arg2_1,
            arg1_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((8,), (1,), torch.float32)
        triton_red_fused_pow_sub_sum_1[grid(8)](arg0_1, arg3_1, buf1, 8, 
            8192, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        del arg0_1
        del arg3_1
        buf3 = buf0
        del buf0
        triton_per_fused_add_div_mul_pow_sub_sum_2[grid(1)](buf3, buf1, 1, 
            8, XBLOCK=1, num_warps=2, num_stages=1)
        del buf1
    return buf3,


class CapsuleLossNew(nn.Module):

    def __init__(self):
        super(CapsuleLossNew, self).__init__()

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
