# 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/vq/cvqsqs346uwxrr2ucuxkvczyso5ne2j3lamclo5tgaqb4vbvxrjt.py
# Topologically Sorted Source Nodes: [scatter_, setitem, mask, mul_, kl_div], Original ATen: [aten.scatter, aten.lift_fresh, aten.fill, aten.ne, aten.mul, aten.xlogy, aten.sub, aten.sum]
# Source node to ATen node mapping:
#   kl_div => eq, full_default_2, full_default_3, isnan, log, mul_1, mul_2, sub, sum_1, where, where_1
#   mask => ne
#   mul_ => mul
#   scatter_ => scatter_upon_const_tensor
#   setitem => copy, full_default_1
# Graph fragment:
#   %scatter_upon_const_tensor : [num_users=2] = call_function[target=torch._inductor.fx_passes.post_grad.scatter_upon_const_tensor](args = (), kwargs = {shape: [4, 2], background_val: 2.0, dtype: torch.float32, dim: 1, selector: %view, val: -3})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_1, %full_default_1), kwargs = {})
#   %select_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%scatter_upon_const_tensor, %copy, 1, 0), kwargs = {})
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%arg1_1, 0), kwargs = {})
#   %mul : [num_users=5] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_scatter_default, %ne), kwargs = {})
#   %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%mul,), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], nan), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%mul, 0), kwargs = {})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %log), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default_2, %mul_2), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan, %full_default_3, %where), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %arg0_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %mul_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub,), kwargs = {})
triton_per_fused_fill_lift_fresh_mul_ne_scatter_sub_sum_xlogy_0 = async_compile.triton('triton_per_fused_fill_lift_fresh_mul_ne_scatter_sub_sum_xlogy_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, 8],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_fill_lift_fresh_mul_ne_scatter_sub_sum_xlogy_0', '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_per_fused_fill_lift_fresh_mul_ne_scatter_sub_sum_xlogy_0(in_ptr0, in_ptr1, out_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 % 2
    r1 = (rindex // 2)
    r2 = rindex
    tmp3 = tl.load(in_ptr0 + (r1), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (r2), None)
    tmp0 = r0
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tmp3 == tmp0
    tmp5 = -3.0
    tmp6 = 2.0
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = 0.0
    tmp9 = tl.where(tmp2, tmp8, tmp7)
    tmp10 = tl.full([1, 1], 0, tl.int64)
    tmp11 = tmp3 != tmp10
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp9 * tmp12
    tmp14 = libdevice.isnan(tmp13).to(tl.int1)
    tmp15 = tmp13 == tmp8
    tmp16 = tl_math.log(tmp13)
    tmp17 = tmp13 * tmp16
    tmp18 = tl.where(tmp15, tmp8, tmp17)
    tmp19 = float("nan")
    tmp20 = tl.where(tmp14, tmp19, tmp18)
    tmp22 = tmp13 * tmp21
    tmp23 = tmp20 - tmp22
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp26, 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, 2), (2, 1))
    assert_size_stride(arg1_1, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [scatter_, setitem, mask, mul_, kl_div], Original ATen: [aten.scatter, aten.lift_fresh, aten.fill, aten.ne, aten.mul, aten.xlogy, aten.sub, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_fill_lift_fresh_mul_ne_scatter_sub_sum_xlogy_0.run(arg1_1, arg0_1, buf0, 1, 8, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


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, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.int64)
    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
from torch import nn


class LabelSmoothing(nn.Module):
    """
    Label Smoothing

    Attributes
    ----------
    criterion   : torch.nn.KLDivLoss
    padding_idx : int
    eps         : float
    n_vocab     : int
    """

    def __init__(self, n_vocab, eps, padding_idx=0):
        """
        Parameters
        ----------
        n_vocab     : int
            size of vocab
        eps         : float
            portion of the one hot that will be taken away
        padding_idx : int
            indicator of which value should be considered as padding
        """
        super(LabelSmoothing, self).__init__()
        self.criterion = nn.KLDivLoss(reduction='sum')
        self.padding_idx = padding_idx
        self.eps = eps
        self.n_vocab = n_vocab

    def forward(self, pred, gold):
        """
        Parameters
        ----------
        pred : 2d tensor (batch_size * seq_len, n_vocab)
        gold : 1d tensor of int (batch_size * seq_len)
        """
        n_vocab, eps, padding_idx = self.n_vocab, self.eps, self.padding_idx
        dist = pred.clone()
        dist.fill_(eps / (n_vocab - 2))
        dist.scatter_(1, gold.view(gold.shape[0], -1), 1 - eps)
        dist[:, padding_idx] = 0
        mask = gold != padding_idx
        dist.mul_(mask.view(dist.shape[0], 1))
        return self.criterion(pred, dist)


def get_inputs():
    return [torch.rand([4, 2]), torch.ones([4, 1], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'n_vocab': 4, 'eps': 4}]

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.triton_helpers import libdevice, math as tl_math
from torch import 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_fill_lift_fresh_mul_ne_scatter_sub_sum_xlogy_0(in_ptr0,
    in_ptr1, out_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 % 2
    r1 = rindex // 2
    r2 = rindex
    tmp3 = tl.load(in_ptr0 + r1, None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + r2, None)
    tmp0 = r0
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tmp3 == tmp0
    tmp5 = -3.0
    tmp6 = 2.0
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = 0.0
    tmp9 = tl.where(tmp2, tmp8, tmp7)
    tmp10 = tl.full([1, 1], 0, tl.int64)
    tmp11 = tmp3 != tmp10
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp9 * tmp12
    tmp14 = libdevice.isnan(tmp13).to(tl.int1)
    tmp15 = tmp13 == tmp8
    tmp16 = tl_math.log(tmp13)
    tmp17 = tmp13 * tmp16
    tmp18 = tl.where(tmp15, tmp8, tmp17)
    tmp19 = float('nan')
    tmp20 = tl.where(tmp14, tmp19, tmp18)
    tmp22 = tmp13 * tmp21
    tmp23 = tmp20 - tmp22
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp26, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 2), (2, 1))
    assert_size_stride(arg1_1, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_fill_lift_fresh_mul_ne_scatter_sub_sum_xlogy_0[grid(1)
            ](arg1_1, arg0_1, buf0, 1, 8, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class LabelSmoothingNew(nn.Module):
    """
    Label Smoothing

    Attributes
    ----------
    criterion   : torch.nn.KLDivLoss
    padding_idx : int
    eps         : float
    n_vocab     : int
    """

    def __init__(self, n_vocab, eps, padding_idx=0):
        """
        Parameters
        ----------
        n_vocab     : int
            size of vocab
        eps         : float
            portion of the one hot that will be taken away
        padding_idx : int
            indicator of which value should be considered as padding
        """
        super(LabelSmoothingNew, self).__init__()
        self.criterion = nn.KLDivLoss(reduction='sum')
        self.padding_idx = padding_idx
        self.eps = eps
        self.n_vocab = n_vocab

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