# 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/ch/cchw4r62r5bnmw7dkm2r4jxdawjyon644tohbofuekisgaimhe7b.py
# Topologically Sorted Source Nodes: [mul, log_gaussian_mean, log_gaussian_mean_1, sum_1, constant], Original ATen: [aten.mul, aten.add, aten.exp, aten.sum]
# Source node to ATen node mapping:
#   constant => add_1
#   log_gaussian_mean => add
#   log_gaussian_mean_1 => exp
#   mul => mul
#   sum_1 => sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.5), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, %mul), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%add,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1]), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-05), kwargs = {})
triton_poi_fused_add_exp_mul_sum_0 = async_compile.triton('triton_poi_fused_add_exp_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp6 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp13 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp19 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp8 = tmp7 * tmp2
    tmp9 = tmp6 + tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp5 + tmp10
    tmp14 = tmp13 * tmp2
    tmp15 = tmp12 + tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp11 + tmp16
    tmp20 = tmp19 * tmp2
    tmp21 = tmp18 + tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp17 + tmp22
    tmp24 = 1e-05
    tmp25 = tmp23 + tmp24
    tl.store(out_ptr0 + (x2), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7v/c7vyow4rdwzqjpwa5sb57m2yvdgs2p724s63sfflruxo2ubujcld.py
# Topologically Sorted Source Nodes: [mul, log_gaussian_mean, log_gaussian_mean_1, outputs_mean, log_gaussian_variance, log_gaussian_variance_1, exp_2, sub, log_gaussian_variance_2, pow_1, outputs_variance], Original ATen: [aten.mul, aten.add, aten.exp, aten.div, aten.sub, aten.pow]
# Source node to ATen node mapping:
#   exp_2 => exp_2
#   log_gaussian_mean => add
#   log_gaussian_mean_1 => exp
#   log_gaussian_variance => mul_1
#   log_gaussian_variance_1 => exp_1
#   log_gaussian_variance_2 => mul_2
#   mul => mul
#   outputs_mean => div
#   outputs_variance => div_1
#   pow_1 => pow_1
#   sub => sub
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.5), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, %mul), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%add,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %unsqueeze), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 2), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_1,), kwargs = {})
#   %exp_2 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg0_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%exp_2, 1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp_1, %sub), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%unsqueeze, 2), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_2, %pow_1), kwargs = {})
triton_poi_fused_add_div_exp_mul_pow_sub_1 = async_compile.triton('triton_poi_fused_add_div_exp_mul_pow_sub_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: '*fp32', 3: '*fp32', 4: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_exp_mul_pow_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_div_exp_mul_pow_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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_ptr1 + (x3), xmask)
    tmp6 = tl.load(in_ptr2 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tmp8 = 2.0
    tmp9 = tmp4 * tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl_math.exp(tmp1)
    tmp12 = 1.0
    tmp13 = tmp11 - tmp12
    tmp14 = tmp10 * tmp13
    tmp15 = tmp6 * tmp6
    tmp16 = tmp14 / tmp15
    tl.store(out_ptr0 + (x3), tmp7, xmask)
    tl.store(out_ptr1 + (x3), tmp16, xmask)
''', 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)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, log_gaussian_mean, log_gaussian_mean_1, sum_1, constant], Original ATen: [aten.mul, aten.add, aten.exp, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_exp_mul_sum_0.run(arg1_1, arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, log_gaussian_mean, log_gaussian_mean_1, outputs_mean, log_gaussian_variance, log_gaussian_variance_1, exp_2, sub, log_gaussian_variance_2, pow_1, outputs_variance], Original ATen: [aten.mul, aten.add, aten.exp, aten.div, aten.sub, aten.pow]
        triton_poi_fused_add_div_exp_mul_pow_sub_1.run(arg1_1, arg0_1, buf0, buf1, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
    return (buf1, buf2, )


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


def keep_variance_fn(x):
    return x + 0.001


class Softmax(nn.Module):

    def __init__(self, dim=1, keep_variance_fn=None):
        super(Softmax, self).__init__()
        self.dim = dim
        self._keep_variance_fn = keep_variance_fn

    def forward(self, features_mean, features_variance, eps=1e-05):
        """Softmax function applied to a multivariate Gaussian distribution.
        It works under the assumption that features_mean and features_variance
        are the parameters of a the indepent gaussians that contribute to the
        multivariate gaussian.
        Mean and variance of the log-normal distribution are computed following
        https://en.wikipedia.org/wiki/Log-normal_distribution."""
        log_gaussian_mean = features_mean + 0.5 * features_variance
        log_gaussian_variance = 2 * log_gaussian_mean
        log_gaussian_mean = torch.exp(log_gaussian_mean)
        log_gaussian_variance = torch.exp(log_gaussian_variance)
        log_gaussian_variance = log_gaussian_variance * (torch.exp(
            features_variance) - 1)
        constant = torch.sum(log_gaussian_mean, dim=self.dim) + eps
        constant = constant.unsqueeze(self.dim)
        outputs_mean = log_gaussian_mean / constant
        outputs_variance = log_gaussian_variance / constant ** 2
        if self._keep_variance_fn is not None:
            outputs_variance = self._keep_variance_fn(outputs_variance)
        return outputs_mean, outputs_variance


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.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_poi_fused_add_exp_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp8 = tmp7 * tmp2
    tmp9 = tmp6 + tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp5 + tmp10
    tmp14 = tmp13 * tmp2
    tmp15 = tmp12 + tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp11 + tmp16
    tmp20 = tmp19 * tmp2
    tmp21 = tmp18 + tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp17 + tmp22
    tmp24 = 1e-05
    tmp25 = tmp23 + tmp24
    tl.store(out_ptr0 + x2, tmp25, xmask)


@triton.jit
def triton_poi_fused_add_div_exp_mul_pow_sub_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, 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_ptr1 + x3, xmask)
    tmp6 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tmp8 = 2.0
    tmp9 = tmp4 * tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl_math.exp(tmp1)
    tmp12 = 1.0
    tmp13 = tmp11 - tmp12
    tmp14 = tmp10 * tmp13
    tmp15 = tmp6 * tmp6
    tmp16 = tmp14 / tmp15
    tl.store(out_ptr0 + x3, tmp7, xmask)
    tl.store(out_ptr1 + x3, tmp16, xmask)


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)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_mul_sum_0[grid(64)](arg1_1, arg0_1, buf0, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_exp_mul_pow_sub_1[grid(256)](arg1_1,
            arg0_1, buf0, buf1, buf2, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
    return buf1, buf2


def keep_variance_fn(x):
    return x + 0.001


class SoftmaxNew(nn.Module):

    def __init__(self, dim=1, keep_variance_fn=None):
        super(SoftmaxNew, self).__init__()
        self.dim = dim
        self._keep_variance_fn = keep_variance_fn

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1]
