# 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_1/inductor_cache/yh/cyh2ll5mee5wqmmcim72iqhtmld7a2fc75dktzhdl3yvdq3o3fte.py
# Topologically Sorted Source Nodes: [add_2, log_1, add, add_1, log_theta_mu_eps, sub, mul, add_3, log_2, sub_1, mul_1, add_4, add_5, lgamma, add_6, lgamma_1, sub_2, add_7, lgamma_2, res, res_1], Original ATen: [aten.add, aten.log, aten.sub, aten.mul, aten.lgamma, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   add_4 => add_4
#   add_5 => add_5
#   add_6 => add_6
#   add_7 => add_7
#   lgamma => lgamma
#   lgamma_1 => lgamma_1
#   lgamma_2 => lgamma_2
#   log_1 => log_1
#   log_2 => log_2
#   log_theta_mu_eps => log
#   mul => mul
#   mul_1 => mul_1
#   res => sub_3
#   res_1 => mean
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
# Graph fragment:
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1e-08), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_2,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, 1e-08), kwargs = {})
#   %log : [num_users=2] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%log_1, %log), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %sub), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, 1e-08), kwargs = {})
#   %log_2 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_3,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%log_2, %log), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, %sub_1), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg2_1, %arg0_1), kwargs = {})
#   %lgamma : [num_users=1] = call_function[target=torch.ops.aten.lgamma.default](args = (%add_5,), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %lgamma), kwargs = {})
#   %lgamma_1 : [num_users=1] = call_function[target=torch.ops.aten.lgamma.default](args = (%arg0_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_6, %lgamma_1), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg2_1, 1), kwargs = {})
#   %lgamma_2 : [num_users=1] = call_function[target=torch.ops.aten.lgamma.default](args = (%add_7,), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %lgamma_2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_3,), kwargs = {})
triton_per_fused_add_lgamma_log_mean_mul_sub_0 = async_compile.triton('triton_per_fused_add_lgamma_log_mean_mul_sub_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: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_add_lgamma_log_mean_mul_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_lgamma_log_mean_mul_sub_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    tmp4 = tl.load(in_ptr1 + (r0), None)
    tmp10 = tl.load(in_ptr2 + (r0), None)
    tmp1 = 1e-08
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.log(tmp2)
    tmp5 = tmp0 + tmp4
    tmp6 = tmp5 + tmp1
    tmp7 = tl_math.log(tmp6)
    tmp8 = tmp3 - tmp7
    tmp9 = tmp0 * tmp8
    tmp11 = tmp4 + tmp1
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp12 - tmp7
    tmp14 = tmp10 * tmp13
    tmp15 = tmp9 + tmp14
    tmp16 = tmp10 + tmp0
    tmp17 = libdevice.lgamma(tmp16)
    tmp18 = tmp15 + tmp17
    tmp19 = libdevice.lgamma(tmp0)
    tmp20 = tmp18 - tmp19
    tmp21 = 1.0
    tmp22 = tmp10 + tmp21
    tmp23 = libdevice.lgamma(tmp22)
    tmp24 = tmp20 - tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = 256.0
    tmp29 = tmp27 / tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp29, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [add_2, log_1, add, add_1, log_theta_mu_eps, sub, mul, add_3, log_2, sub_1, mul_1, add_4, add_5, lgamma, add_6, lgamma_1, sub_2, add_7, lgamma_2, res, res_1], Original ATen: [aten.add, aten.log, aten.sub, aten.mul, aten.lgamma, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_lgamma_log_mean_mul_sub_0.run(buf1, arg0_1, arg1_1, arg2_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
    return (buf1, )


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)
    fn = lambda: call([arg0_1, arg1_1, arg2_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


class NB(torch.nn.Module):
    """
    Yang Comment: Usage in forward:
    x : Ground truth
    mu: Prediction
    theta: Another trainable parameter with shape=[xdim(number of count variables)],
           simply initialize a nn.Parameter(torch.randn(xdim)) in the model

    Be careful, we need the negative of the returned value: loss = -NBLoss
    """

    def __init__(self, eps=1e-08, reduction='mean'):
        super(NB, self).__init__()
        self.eps = eps
        self.reduction = reduction

    def forward(self, x: 'torch.Tensor', mu: 'torch.Tensor', theta:
        'torch.Tensor'):
        if theta.ndimension() == 1:
            theta = theta.view(1, theta.size(0))
        log_theta_mu_eps = torch.log(theta + mu + self.eps)
        res = theta * (torch.log(theta + self.eps) - log_theta_mu_eps) + x * (
            torch.log(mu + self.eps) - log_theta_mu_eps) + torch.lgamma(x +
            theta) - torch.lgamma(theta) - torch.lgamma(x + 1)
        if self.reduction == 'mean':
            res = torch.mean(res)
        elif self.reduction == 'sum':
            res = torch.sum(res)
        else:
            raise NotImplementedError(
                f'reduction method {self.reduction} is not implemented.')
        return res


def get_inputs():
    return [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 libdevice, math as tl_math
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_lgamma_log_mean_mul_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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)
    tmp4 = tl.load(in_ptr1 + r0, None)
    tmp10 = tl.load(in_ptr2 + r0, None)
    tmp1 = 1e-08
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.log(tmp2)
    tmp5 = tmp0 + tmp4
    tmp6 = tmp5 + tmp1
    tmp7 = tl_math.log(tmp6)
    tmp8 = tmp3 - tmp7
    tmp9 = tmp0 * tmp8
    tmp11 = tmp4 + tmp1
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp12 - tmp7
    tmp14 = tmp10 * tmp13
    tmp15 = tmp9 + tmp14
    tmp16 = tmp10 + tmp0
    tmp17 = libdevice.lgamma(tmp16)
    tmp18 = tmp15 + tmp17
    tmp19 = libdevice.lgamma(tmp0)
    tmp20 = tmp18 - tmp19
    tmp21 = 1.0
    tmp22 = tmp10 + tmp21
    tmp23 = libdevice.lgamma(tmp22)
    tmp24 = tmp20 - tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = 256.0
    tmp29 = tmp27 / tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp29, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_lgamma_log_mean_mul_sub_0[grid(1)](buf1,
            arg0_1, arg1_1, arg2_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf1,


class NBNew(torch.nn.Module):
    """
    Yang Comment: Usage in forward:
    x : Ground truth
    mu: Prediction
    theta: Another trainable parameter with shape=[xdim(number of count variables)],
           simply initialize a nn.Parameter(torch.randn(xdim)) in the model

    Be careful, we need the negative of the returned value: loss = -NBLoss
    """

    def __init__(self, eps=1e-08, reduction='mean'):
        super(NBNew, self).__init__()
        self.eps = eps
        self.reduction = reduction

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