# 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/zx/czxeb2salzmudq43t4xuixnmxxnhd3pzuyutohzy7ap3ejizmr5h.py
# Topologically Sorted Source Nodes: [max_1, sub_1, sigmoid_1, log, add, sub, exp, sigmoid, exp_logits_sigmoided, sum_exp_logits_sigmoided, log_1, log_probs], Original ATen: [aten.max, aten.sub, aten.sigmoid, aten.log, aten.add, aten.exp, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   exp => exp
#   exp_logits_sigmoided => mul
#   log => log
#   log_1 => log_1
#   log_probs => sub_2
#   max_1 => max_1
#   sigmoid => sigmoid
#   sigmoid_1 => sigmoid_1
#   sub => sub
#   sub_1 => sub_1
#   sum_exp_logits_sigmoided => sum_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%arg0_1, 1, True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %getitem), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sigmoid_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, %log), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %sigmoid), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1], True), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %log_1), kwargs = {})
triton_poi_fused_add_exp_log_max_mul_sigmoid_sub_sum_0 = async_compile.triton('triton_poi_fused_add_exp_log_max_mul_sigmoid_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.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_add_exp_log_max_mul_sigmoid_sub_sum_0', '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_add_exp_log_max_mul_sigmoid_sub_sum_0(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
    tmp9 = tl.sigmoid(tmp0)
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tmp1 - tmp7
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tl.sigmoid(tmp1)
    tmp15 = tmp13 * tmp14
    tmp16 = tmp2 - tmp7
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tl.sigmoid(tmp2)
    tmp19 = tmp17 * tmp18
    tmp20 = tmp15 + tmp19
    tmp21 = tmp4 - tmp7
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tl.sigmoid(tmp4)
    tmp24 = tmp22 * tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp6 - tmp7
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tl.sigmoid(tmp6)
    tmp29 = tmp27 * tmp28
    tmp30 = tmp25 + tmp29
    tmp31 = tl_math.log(tmp30)
    tmp32 = tmp11 - tmp31
    tl.store(out_ptr0 + (x3), tmp32, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_1, sub_1, sigmoid_1, log, add, sub, exp, sigmoid, exp_logits_sigmoided, sum_exp_logits_sigmoided, log_1, log_probs], Original ATen: [aten.max, aten.sub, aten.sigmoid, aten.log, aten.add, aten.exp, aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_exp_log_max_mul_sigmoid_sub_sum_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


def logsigsoftmax_v1(logits, dim=1):
    """
    Computes sigsoftmax from the paper - https://arxiv.org/pdf/1805.10829.pdf
    """
    max_values = torch.max(logits, dim, keepdim=True)[0]
    exp_logits_sigmoided = torch.exp(logits - max_values) * torch.sigmoid(
        logits)
    sum_exp_logits_sigmoided = exp_logits_sigmoided.sum(1, keepdim=True)
    log_probs = logits - max_values + torch.log(torch.sigmoid(logits)
        ) - torch.log(sum_exp_logits_sigmoided)
    return log_probs


class SigSoftmaxV1(nn.Module):
    """
        Sigmoid 加上 softmax的实现
    """

    def __init__(self, dim=1):
        super().__init__()
        self.dim = dim

    def forward(self, logits):
        return logsigsoftmax_v1(logits, dim=self.dim)


def get_inputs():
    return [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
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_exp_log_max_mul_sigmoid_sub_sum_0(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
    tmp9 = tl.sigmoid(tmp0)
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tmp1 - tmp7
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tl.sigmoid(tmp1)
    tmp15 = tmp13 * tmp14
    tmp16 = tmp2 - tmp7
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tl.sigmoid(tmp2)
    tmp19 = tmp17 * tmp18
    tmp20 = tmp15 + tmp19
    tmp21 = tmp4 - tmp7
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tl.sigmoid(tmp4)
    tmp24 = tmp22 * tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp6 - tmp7
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tl.sigmoid(tmp6)
    tmp29 = tmp27 * tmp28
    tmp30 = tmp25 + tmp29
    tmp31 = tl_math.log(tmp30)
    tmp32 = tmp11 - tmp31
    tl.store(out_ptr0 + x3, tmp32, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_log_max_mul_sigmoid_sub_sum_0[grid(256)](
            arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def logsigsoftmax_v1(logits, dim=1):
    """
    Computes sigsoftmax from the paper - https://arxiv.org/pdf/1805.10829.pdf
    """
    max_values = torch.max(logits, dim, keepdim=True)[0]
    exp_logits_sigmoided = torch.exp(logits - max_values) * torch.sigmoid(
        logits)
    sum_exp_logits_sigmoided = exp_logits_sigmoided.sum(1, keepdim=True)
    log_probs = logits - max_values + torch.log(torch.sigmoid(logits)
        ) - torch.log(sum_exp_logits_sigmoided)
    return log_probs


class SigSoftmaxV1New(nn.Module):
    """
        Sigmoid 加上 softmax的实现
    """

    def __init__(self, dim=1):
        super().__init__()
        self.dim = dim

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