# 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_7/inductor_cache/ig/cigyn6vopknxpazisjpo4zsfoqpts5v47u2w4s3tj7q2z2k3gc3v.py
# Topologically Sorted Source Nodes: [sub, abs_1, distance_positive, sub_1, abs_2, distance_negative, sub_2, add, losses, sum_3, sum_4], Original ATen: [aten.sub, aten.abs, aten.sum, aten.add, aten.relu]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   abs_2 => abs_2
#   add => add
#   distance_negative => sum_2
#   distance_positive => sum_1
#   losses => relu
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sum_3 => sum_5
#   sum_4 => sum_6
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%abs_1, [1]), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg2_1), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%abs_2, [1]), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sum_1, %sum_2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, 0.0), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%relu,), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%relu,), kwargs = {})
triton_per_fused_abs_add_relu_sub_sum_0 = async_compile.triton('triton_per_fused_abs_add_relu_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, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_relu_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 2, '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_abs_add_relu_sub_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp9 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp10 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp14 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp15 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp19 = tl.load(in_ptr2 + (r0 + (64*r1)), None)
    tmp22 = tl.load(in_ptr2 + (16 + r0 + (64*r1)), None)
    tmp26 = tl.load(in_ptr2 + (32 + r0 + (64*r1)), None)
    tmp30 = tl.load(in_ptr2 + (48 + r0 + (64*r1)), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.abs(tmp6)
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.abs(tmp11)
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tl_math.abs(tmp16)
    tmp18 = tmp13 + tmp17
    tmp20 = tmp0 - tmp19
    tmp21 = tl_math.abs(tmp20)
    tmp23 = tmp4 - tmp22
    tmp24 = tl_math.abs(tmp23)
    tmp25 = tmp21 + tmp24
    tmp27 = tmp9 - tmp26
    tmp28 = tl_math.abs(tmp27)
    tmp29 = tmp25 + tmp28
    tmp31 = tmp14 - tmp30
    tmp32 = tl_math.abs(tmp31)
    tmp33 = tmp29 + tmp32
    tmp34 = tmp18 - tmp33
    tmp35 = 0.0
    tmp36 = tmp34 + tmp35
    tmp37 = tl.full([1, 1], 0, tl.int32)
    tmp38 = triton_helpers.maximum(tmp37, tmp36)
    tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41 = tl.sum(tmp39, 1)[:, None]
    tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp41, None)
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bg/cbg32drchyezvbfwshguvyopixmzwi2llws7xkhvpdruis76tr2t.py
# Topologically Sorted Source Nodes: [loss_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   loss_softmax => amax, sub_3
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg4_1, [1], True), kwargs = {})
#   %sub_3 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg4_1, %amax), kwargs = {})
triton_poi_fused__log_softmax_1 = async_compile.triton('triton_poi_fused__log_softmax_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=[256], 
    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__log_softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kg/ckgd2kctqmwsk45sik2irjt4vawej5bf75jszty3x7ik4kc4vcmh.py
# Topologically Sorted Source Nodes: [mul, loss_softmax, loss_total], Original ATen: [aten.mul, aten._log_softmax, aten.sum, aten.neg, aten.div, aten.add]
# Source node to ATen node mapping:
#   loss_softmax => div, exp, log, mul, neg, sub_4, sum_3, sum_4
#   loss_total => add_1
#   mul => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_5, 0.01), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_3,), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_3, %log), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %arg3_1), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_4,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Scalar](args = (%neg, 64), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %div), kwargs = {})
triton_per_fused__log_softmax_add_div_mul_neg_sum_2 = async_compile.triton('triton_per_fused__log_softmax_add_div_mul_neg_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, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_add_div_mul_neg_sum_2', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 7, '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__log_softmax_add_div_mul_neg_sum_2(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, 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)
    r3 = rindex
    r0 = rindex % 16
    r2 = (rindex // 64)
    tmp0 = tl.load(in_ptr0 + (r3), None)
    tmp1 = tl.load(in_ptr0 + (r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (r3), None)
    tmp22 = tl.load(in_out_ptr1 + (0))
    tmp23 = tl.broadcast_to(tmp22, [1])
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp19 = -tmp18
    tmp20 = 0.015625
    tmp21 = tmp19 * tmp20
    tmp24 = 0.01
    tmp25 = tmp23 * tmp24
    tmp26 = tmp25 + tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp21, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([1], 0, tl.int32)), tmp26, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 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, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf5 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [sub, abs_1, distance_positive, sub_1, abs_2, distance_negative, sub_2, add, losses, sum_3, sum_4], Original ATen: [aten.sub, aten.abs, aten.sum, aten.add, aten.relu]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_relu_sub_sum_0.run(arg0_1, arg1_1, arg2_1, buf1, buf5, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [loss_softmax], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_1.run(arg4_1, buf2, 256, grid=grid(256), stream=stream0)
        del arg4_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        buf6 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul, loss_softmax, loss_total], Original ATen: [aten.mul, aten._log_softmax, aten.sum, aten.neg, aten.div, aten.add]
        triton_per_fused__log_softmax_add_div_mul_neg_sum_2.run(buf4, buf6, buf2, arg3_1, 1, 256, grid=grid(1), stream=stream0)
        del arg3_1
        del buf2
    return (buf6, buf5, buf4, )


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, 4), (64, 16, 4, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg4_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_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 TripletSoftmaxLoss(nn.Module):

    def __init__(self, margin=0.0, lambda_factor=0.01):
        super(TripletSoftmaxLoss, self).__init__()
        self.margin = margin
        self.loss_fn = nn.CrossEntropyLoss()
        self.lambda_factor = lambda_factor

    def forward(self, anchor, positive, negative, outputs, labels):
        distance_positive = torch.abs(anchor - positive).sum(1)
        distance_negative = torch.abs(anchor - negative).sum(1)
        losses = F.relu(distance_positive - distance_negative + self.margin)
        loss_softmax = self.loss_fn(input=outputs, target=labels)
        loss_total = self.lambda_factor * losses.sum() + loss_softmax
        return loss_total, losses.sum(), loss_softmax


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


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
from torch._inductor.runtime.triton_helpers import math as tl_math
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_abs_add_relu_sub_sum_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp9 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp14 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp19 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp22 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp26 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp30 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.abs(tmp6)
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.abs(tmp11)
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tl_math.abs(tmp16)
    tmp18 = tmp13 + tmp17
    tmp20 = tmp0 - tmp19
    tmp21 = tl_math.abs(tmp20)
    tmp23 = tmp4 - tmp22
    tmp24 = tl_math.abs(tmp23)
    tmp25 = tmp21 + tmp24
    tmp27 = tmp9 - tmp26
    tmp28 = tl_math.abs(tmp27)
    tmp29 = tmp25 + tmp28
    tmp31 = tmp14 - tmp30
    tmp32 = tl_math.abs(tmp31)
    tmp33 = tmp29 + tmp32
    tmp34 = tmp18 - tmp33
    tmp35 = 0.0
    tmp36 = tmp34 + tmp35
    tmp37 = tl.full([1, 1], 0, tl.int32)
    tmp38 = triton_helpers.maximum(tmp37, tmp36)
    tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41 = tl.sum(tmp39, 1)[:, None]
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp41, None)
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp41, None)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_per_fused__log_softmax_add_div_mul_neg_sum_2(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, 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)
    r3 = rindex
    r0 = rindex % 16
    r2 = rindex // 64
    tmp0 = tl.load(in_ptr0 + r3, None)
    tmp1 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr1 + r3, None)
    tmp22 = tl.load(in_out_ptr1 + 0)
    tmp23 = tl.broadcast_to(tmp22, [1])
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp19 = -tmp18
    tmp20 = 0.015625
    tmp21 = tmp19 * tmp20
    tmp24 = 0.01
    tmp25 = tmp23 * tmp24
    tmp26 = tmp25 + tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp21, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([1], 0, tl.int32), tmp26, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 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, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf5 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_add_relu_sub_sum_0[grid(1)](arg0_1, arg1_1,
            arg2_1, buf1, buf5, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax_1[grid(256)](arg4_1, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg4_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        buf6 = buf1
        del buf1
        triton_per_fused__log_softmax_add_div_mul_neg_sum_2[grid(1)](buf4,
            buf6, buf2, arg3_1, 1, 256, num_warps=2, num_stages=1)
        del arg3_1
        del buf2
    return buf6, buf5, buf4


class TripletSoftmaxLossNew(nn.Module):

    def __init__(self, margin=0.0, lambda_factor=0.01):
        super(TripletSoftmaxLossNew, self).__init__()
        self.margin = margin
        self.loss_fn = nn.CrossEntropyLoss()
        self.lambda_factor = lambda_factor

    def forward(self, input_0, input_1, input_2, input_3, input_4):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        arg4_1 = input_4
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0], output[1], output[2]
