# 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/kx/ckxbici5enkdj4xkrzyuktsnt752p5pl3oknaqb4ion6p76ptspy.py
# Topologically Sorted Source Nodes: [sub, truediv, pow_1, neg, truediv_1, exp, wrapped_sqrt, mul, distrib, mul_1, distrib_1, log, mean, loss], Original ATen: [aten.sub, aten.div, aten.pow, aten.neg, aten.exp, aten.sqrt, aten.mul, aten.sum, aten.log, aten.mean]
# Source node to ATen node mapping:
#   distrib => div_2
#   distrib_1 => sum_1
#   exp => exp
#   log => log
#   loss => neg_1
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   neg => neg
#   pow_1 => pow_1
#   sub => sub
#   truediv => div
#   truediv_1 => div_1
#   wrapped_sqrt => full_default
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %arg1_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %arg2_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%div, 2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%pow_1,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, 2), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%div_1,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 2.5066282746310002), kwargs = {dtype: torch.float64, layout: torch.strided, device: cpu, pin_memory: False})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, %full_default), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %mul), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg3_1, %div_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [3]), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%log,), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean,), kwargs = {})
triton_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_sub_sum_0 = async_compile.triton('triton_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_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.reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 16, '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_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp59 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex % 4
        r1 = (rindex // 4) % 16
        r4 = (rindex // 4)
        r3 = rindex
        tmp0 = tl.load(in_ptr0 + (r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + (4*r4), rmask, eviction_policy='evict_last', other=0.0)
        tmp2 = tl.load(in_ptr2 + (r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp4 = tl.load(in_ptr3 + (r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp15 = tl.load(in_ptr0 + (4 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp16 = tl.load(in_ptr1 + (1 + (4*r4)), rmask, eviction_policy='evict_last', other=0.0)
        tmp17 = tl.load(in_ptr2 + (4 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp19 = tl.load(in_ptr3 + (4 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp29 = tl.load(in_ptr0 + (8 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp30 = tl.load(in_ptr1 + (2 + (4*r4)), rmask, eviction_policy='evict_last', other=0.0)
        tmp31 = tl.load(in_ptr2 + (8 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp33 = tl.load(in_ptr3 + (8 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp43 = tl.load(in_ptr0 + (12 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp44 = tl.load(in_ptr1 + (3 + (4*r4)), rmask, eviction_policy='evict_last', other=0.0)
        tmp45 = tl.load(in_ptr2 + (12 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp47 = tl.load(in_ptr3 + (12 + r0 + (16*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp3 = tmp1 - tmp2
        tmp5 = tmp3 / tmp4
        tmp6 = tmp5 * tmp5
        tmp7 = -tmp6
        tmp8 = 0.5
        tmp9 = tmp7 * tmp8
        tmp10 = tl_math.exp(tmp9)
        tmp11 = 2.5066282746310002
        tmp12 = tmp4 * tmp11
        tmp13 = tmp10 / tmp12
        tmp14 = tmp0 * tmp13
        tmp18 = tmp16 - tmp17
        tmp20 = tmp18 / tmp19
        tmp21 = tmp20 * tmp20
        tmp22 = -tmp21
        tmp23 = tmp22 * tmp8
        tmp24 = tl_math.exp(tmp23)
        tmp25 = tmp19 * tmp11
        tmp26 = tmp24 / tmp25
        tmp27 = tmp15 * tmp26
        tmp28 = tmp14 + tmp27
        tmp32 = tmp30 - tmp31
        tmp34 = tmp32 / tmp33
        tmp35 = tmp34 * tmp34
        tmp36 = -tmp35
        tmp37 = tmp36 * tmp8
        tmp38 = tl_math.exp(tmp37)
        tmp39 = tmp33 * tmp11
        tmp40 = tmp38 / tmp39
        tmp41 = tmp29 * tmp40
        tmp42 = tmp28 + tmp41
        tmp46 = tmp44 - tmp45
        tmp48 = tmp46 / tmp47
        tmp49 = tmp48 * tmp48
        tmp50 = -tmp49
        tmp51 = tmp50 * tmp8
        tmp52 = tl_math.exp(tmp51)
        tmp53 = tmp47 * tmp11
        tmp54 = tmp52 / tmp53
        tmp55 = tmp43 * tmp54
        tmp56 = tmp42 + tmp55
        tmp57 = tl_math.log(tmp56)
        tmp58 = tl.broadcast_to(tmp57, [XBLOCK, RBLOCK])
        tmp60 = _tmp59 + tmp58
        _tmp59 = tl.where(rmask, tmp60, _tmp59)
    tmp59 = tl.sum(_tmp59, 1)[:, None]
    tmp61 = 256.0
    tmp62 = tmp59 / tmp61
    tmp63 = -tmp62
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp63, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub, truediv, pow_1, neg, truediv_1, exp, wrapped_sqrt, mul, distrib, mul_1, distrib_1, log, mean, loss], Original ATen: [aten.sub, aten.div, aten.pow, aten.neg, aten.exp, aten.sqrt, aten.mul, aten.sum, aten.log, aten.mean]
        stream0 = get_raw_stream(0)
        triton_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_sub_sum_0.run(buf2, arg3_1, arg0_1, arg1_1, arg2_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return (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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_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, arg3_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 numpy as np
import torch.nn as nn


class GMMLoss(nn.Module):

    def __init__(self):
        super(GMMLoss, self).__init__()

    def forward(self, x, mu, std, pi):
        x = x.unsqueeze(-1)
        distrib = torch.exp(-((x - mu) / std) ** 2 / 2) / (std * np.sqrt(2 *
            np.pi))
        distrib = torch.sum(pi * distrib, dim=3)
        loss = -torch.log(distrib).mean()
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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.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_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK: tl.
    constexpr, RBLOCK: tl.constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp59 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex % 4
        r1 = rindex // 4 % 16
        r4 = rindex // 4
        tmp0 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + 4 * r4, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp2 = tl.load(in_ptr2 + (r0 + 16 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp4 = tl.load(in_ptr3 + (r0 + 16 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp15 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp16 = tl.load(in_ptr1 + (1 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp17 = tl.load(in_ptr2 + (4 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp19 = tl.load(in_ptr3 + (4 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp29 = tl.load(in_ptr0 + (8 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp30 = tl.load(in_ptr1 + (2 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp31 = tl.load(in_ptr2 + (8 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp33 = tl.load(in_ptr3 + (8 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp43 = tl.load(in_ptr0 + (12 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp44 = tl.load(in_ptr1 + (3 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp45 = tl.load(in_ptr2 + (12 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp47 = tl.load(in_ptr3 + (12 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp3 = tmp1 - tmp2
        tmp5 = tmp3 / tmp4
        tmp6 = tmp5 * tmp5
        tmp7 = -tmp6
        tmp8 = 0.5
        tmp9 = tmp7 * tmp8
        tmp10 = tl_math.exp(tmp9)
        tmp11 = 2.5066282746310002
        tmp12 = tmp4 * tmp11
        tmp13 = tmp10 / tmp12
        tmp14 = tmp0 * tmp13
        tmp18 = tmp16 - tmp17
        tmp20 = tmp18 / tmp19
        tmp21 = tmp20 * tmp20
        tmp22 = -tmp21
        tmp23 = tmp22 * tmp8
        tmp24 = tl_math.exp(tmp23)
        tmp25 = tmp19 * tmp11
        tmp26 = tmp24 / tmp25
        tmp27 = tmp15 * tmp26
        tmp28 = tmp14 + tmp27
        tmp32 = tmp30 - tmp31
        tmp34 = tmp32 / tmp33
        tmp35 = tmp34 * tmp34
        tmp36 = -tmp35
        tmp37 = tmp36 * tmp8
        tmp38 = tl_math.exp(tmp37)
        tmp39 = tmp33 * tmp11
        tmp40 = tmp38 / tmp39
        tmp41 = tmp29 * tmp40
        tmp42 = tmp28 + tmp41
        tmp46 = tmp44 - tmp45
        tmp48 = tmp46 / tmp47
        tmp49 = tmp48 * tmp48
        tmp50 = -tmp49
        tmp51 = tmp50 * tmp8
        tmp52 = tl_math.exp(tmp51)
        tmp53 = tmp47 * tmp11
        tmp54 = tmp52 / tmp53
        tmp55 = tmp43 * tmp54
        tmp56 = tmp42 + tmp55
        tmp57 = tl_math.log(tmp56)
        tmp58 = tl.broadcast_to(tmp57, [XBLOCK, RBLOCK])
        tmp60 = _tmp59 + tmp58
        _tmp59 = tl.where(rmask, tmp60, _tmp59)
    tmp59 = tl.sum(_tmp59, 1)[:, None]
    tmp61 = 256.0
    tmp62 = tmp59 / tmp61
    tmp63 = -tmp62
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp63, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_sub_sum_0[grid(1)](
            buf2, arg3_1, arg0_1, arg1_1, arg2_1, 1, 256, XBLOCK=1, RBLOCK=
            256, num_warps=8, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf2,


class GMMLossNew(nn.Module):

    def __init__(self):
        super(GMMLossNew, self).__init__()

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
