# 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/me/cmeuxvrqkxc6mh45iek5o4xscscfms4pj3vilkl5gxhqaakotow6.py
# Topologically Sorted Source Nodes: [labels_1, logits_1, softmax, add, log, mul, sum_1], Original ATen: [aten.cat, aten._softmax, aten.add, aten.log, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   labels_1 => cat_1
#   log => log
#   logits_1 => cat
#   mul => mul
#   softmax => amax, div, exp, sub, sum_1
#   sum_1 => sum_2
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%slice_3, %slice_7], -1), kwargs = {})
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%slice_1, %slice_5], -1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%cat, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cat, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, 2.220446049250313e-16), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cat_1, %log), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
triton_per_fused__softmax_add_cat_log_mul_sum_0 = async_compile.triton('triton_per_fused__softmax_add_cat_log_mul_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=[32, 8],
    reduction_hint=ReductionHint.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_per_fused__softmax_add_cat_log_mul_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 3, '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__softmax_add_cat_log_mul_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    rnumel = 8
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = (xindex // 16)
    x3 = xindex
    tmp0 = r2
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x0) + (128*x1) + r2), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1, 1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + (64 + (4*x0) + (128*x1) + ((-4) + r2)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, float("-inf"))
    tmp14 = triton_helpers.max2(tmp13, 1)[:, None]
    tmp15 = tmp10 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.where(xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tmp21 = tl.load(in_ptr1 + ((4*x0) + (128*x1) + r2), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp22 = tl.load(in_ptr1 + (64 + (4*x0) + (128*x1) + ((-4) + r2)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = tl.where(tmp4, tmp21, tmp22)
    tmp24 = tmp16 / tmp20
    tmp25 = 2.220446049250313e-16
    tmp26 = tmp24 + tmp25
    tmp27 = tl_math.log(tmp26)
    tmp28 = tmp23 * tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.where(xmask, tmp29, 0)
    tmp32 = tl.sum(tmp31, 1)[:, None]
    tl.store(in_out_ptr0 + (x3), tmp32, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7p/c7pbkcwg7cfjh56cn3osmbco7jlahdskaykb3cahgt7dxyo6r6md.py
# Topologically Sorted Source Nodes: [mean, neg], Original ATen: [aten.mean, aten.neg]
# Source node to ATen node mapping:
#   mean => mean
#   neg => neg
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean,), kwargs = {})
triton_per_fused_mean_neg_1 = async_compile.triton('triton_per_fused_mean_neg_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.persistent_reduction(
    size_hints=[1, 32],
    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, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_neg_1', 'mutated_arg_names': ['in_out_ptr0'], '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_per_fused_mean_neg_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 32
    RBLOCK: tl.constexpr = 32
    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)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp4 = 32.0
    tmp5 = tmp3 / tmp4
    tmp6 = -tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((2, 4, 4, 1), (16, 4, 1, 32), torch.float32)
        buf2 = reinterpret_tensor(buf1, (2, 4, 4), (16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [labels_1, logits_1, softmax, add, log, mul, sum_1], Original ATen: [aten.cat, aten._softmax, aten.add, aten.log, aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused__softmax_add_cat_log_mul_sum_0.run(buf2, arg0_1, arg1_1, 32, 8, grid=grid(32), stream=stream0)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [mean, neg], Original ATen: [aten.mean, aten.neg]
        triton_per_fused_mean_neg_1.run(buf4, buf2, 1, 32, grid=grid(1), stream=stream0)
        del buf2
    return (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)
    fn = lambda: call([arg0_1, arg1_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 RankCrossEntropyLoss(nn.Module):
    """Creates a criterion that measures rank cross entropy loss."""
    __constants__ = ['num_neg']

    def __init__(self, num_neg: 'int'=1):
        """
        :class:`RankCrossEntropyLoss` constructor.

        :param num_neg: Number of negative instances in hinge loss.
        """
        super().__init__()
        self.num_neg = num_neg

    def forward(self, y_pred: 'torch.Tensor', y_true: 'torch.Tensor'):
        """
        Calculate rank cross entropy loss.

        :param y_pred: Predicted result.
        :param y_true: Label.
        :return: Rank cross loss.
        """
        logits = y_pred[::self.num_neg + 1, :]
        labels = y_true[::self.num_neg + 1, :]
        for neg_idx in range(self.num_neg):
            neg_logits = y_pred[neg_idx + 1::self.num_neg + 1, :]
            neg_labels = y_true[neg_idx + 1::self.num_neg + 1, :]
            logits = torch.cat((logits, neg_logits), dim=-1)
            labels = torch.cat((labels, neg_labels), dim=-1)
        return -torch.mean(torch.sum(labels * torch.log(F.softmax(logits,
            dim=-1) + torch.finfo(float).eps), dim=-1))

    @property
    def num_neg(self):
        """`num_neg` getter."""
        return self._num_neg

    @num_neg.setter
    def num_neg(self, value):
        """`num_neg` setter."""
        self._num_neg = value


def get_inputs():
    return [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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused__softmax_add_cat_log_mul_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = xindex // 16
    x3 = xindex
    tmp0 = r2
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x0 + 128 * x1 + r2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1, 1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (64 + 4 * x0 + 128 * x1 + (-4 + r2)), tmp6 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, float('-inf'))
    tmp14 = triton_helpers.max2(tmp13, 1)[:, None]
    tmp15 = tmp10 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.where(xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tmp21 = tl.load(in_ptr1 + (4 * x0 + 128 * x1 + r2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp22 = tl.load(in_ptr1 + (64 + 4 * x0 + 128 * x1 + (-4 + r2)), tmp6 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = tl.where(tmp4, tmp21, tmp22)
    tmp24 = tmp16 / tmp20
    tmp25 = 2.220446049250313e-16
    tmp26 = tmp24 + tmp25
    tmp27 = tl_math.log(tmp26)
    tmp28 = tmp23 * tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.where(xmask, tmp29, 0)
    tmp32 = tl.sum(tmp31, 1)[:, None]
    tl.store(in_out_ptr0 + x3, tmp32, xmask)


@triton.jit
def triton_per_fused_mean_neg_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 32
    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)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp4 = 32.0
    tmp5 = tmp3 / tmp4
    tmp6 = -tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((2, 4, 4, 1), (16, 4, 1, 32), torch.float32)
        buf2 = reinterpret_tensor(buf1, (2, 4, 4), (16, 4, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_per_fused__softmax_add_cat_log_mul_sum_0[grid(32)](buf2,
            arg0_1, arg1_1, 32, 8, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused_mean_neg_1[grid(1)](buf4, buf2, 1, 32, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf2
    return buf4,


class RankCrossEntropyLossNew(nn.Module):
    """Creates a criterion that measures rank cross entropy loss."""
    __constants__ = ['num_neg']

    def __init__(self, num_neg: 'int'=1):
        """
        :class:`RankCrossEntropyLoss` constructor.

        :param num_neg: Number of negative instances in hinge loss.
        """
        super().__init__()
        self.num_neg = num_neg

    @property
    def num_neg(self):
        """`num_neg` getter."""
        return self._num_neg

    @num_neg.setter
    def num_neg(self, value):
        """`num_neg` setter."""
        self._num_neg = value

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