# AOT ID: ['0_forward']
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/rk/crkf62gn566poghbdxlzmg7dulwy2vx6hyknt4yzw6bx5roqbiom.py
# Topologically Sorted Source Nodes: [pow_1, nu2, abs_1, add, rsqrt, x, mul_1, add_1, max_1], Original ATen: [aten.pow, aten.mean, aten.abs, aten.add, aten.rsqrt, aten.mul, aten.maximum]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   add => add
#   add_1 => add_1
#   max_1 => maximum
#   mul_1 => mul_1
#   nu2 => mean
#   pow_1 => pow_1
#   rsqrt => rsqrt
#   x => mul
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [2, 3], True), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%primals_2,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %abs_1), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %mul), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_4), kwargs = {})
#   %maximum : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%add_1, %primals_5), kwargs = {})
triton_per_fused_abs_add_maximum_mean_mul_pow_rsqrt_0 = async_compile.triton('triton_per_fused_abs_add_maximum_mean_mul_pow_rsqrt_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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: '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, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_maximum_mean_mul_pow_rsqrt_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_abs_add_maximum_mean_mul_pow_rsqrt_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp8 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr4 + (x2), xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = 16.0
    tmp7 = tmp5 / tmp6
    tmp9 = tl_math.abs(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tmp13 = tmp0 * tmp11
    tmp14 = tmp12 * tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp11, xmask)
    tl.store(out_ptr0 + (r1 + (16*x0)), tmp18, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, nu2, abs_1, add, rsqrt, x, mul_1, add_1, max_1], Original ATen: [aten.pow, aten.mean, aten.abs, aten.add, aten.rsqrt, aten.mul, aten.maximum]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_maximum_mean_mul_pow_rsqrt_0.run(buf1, primals_1, primals_2, primals_3, primals_4, primals_5, buf2, 16, 16, grid=grid(16), stream=stream0)
        del primals_2
    return (buf2, primals_1, primals_3, primals_4, primals_5, buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
import torch.utils.data.distributed


class FilterResponseNormNd(nn.Module):

    def __init__(self, ndim, num_features, eps=1e-06, learnable_eps=False):
        """
        Input Variables:
        ----------------
            ndim: An integer indicating the number of dimensions of the expected input tensor.
            num_features: An integer indicating the number of input feature dimensions.
            eps: A scalar constant or learnable variable.
            learnable_eps: A bool value indicating whether the eps is learnable.
        """
        assert ndim in [3, 4, 5
            ], 'FilterResponseNorm only supports 3d, 4d or 5d inputs.'
        super(FilterResponseNormNd, self).__init__()
        shape = (1, num_features) + (1,) * (ndim - 2)
        self.eps = nn.Parameter(torch.ones(*shape) * eps)
        if not learnable_eps:
            self.eps.requires_grad_(False)
        self.gamma = nn.Parameter(torch.Tensor(*shape))
        self.beta = nn.Parameter(torch.Tensor(*shape))
        self.tau = nn.Parameter(torch.Tensor(*shape))
        self.reset_parameters()

    def forward(self, x):
        avg_dims = tuple(range(2, x.dim()))
        nu2 = torch.pow(x, 2).mean(dim=avg_dims, keepdim=True)
        x = x * torch.rsqrt(nu2 + torch.abs(self.eps))
        return torch.max(self.gamma * x + self.beta, self.tau)

    def reset_parameters(self):
        nn.init.ones_(self.gamma)
        nn.init.zeros_(self.beta)
        nn.init.zeros_(self.tau)


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'ndim': 4, 'num_features': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_abs_add_maximum_mean_mul_pow_rsqrt_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp8 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = 16.0
    tmp7 = tmp5 / tmp6
    tmp9 = tl_math.abs(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tmp13 = tmp0 * tmp11
    tmp14 = tmp12 * tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp11, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp18, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_add_maximum_mean_mul_pow_rsqrt_0[grid(16)](buf1,
            primals_1, primals_2, primals_3, primals_4, primals_5, buf2, 16,
            16, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_2
    return buf2, primals_1, primals_3, primals_4, primals_5, buf1


class FilterResponseNormNdNew(nn.Module):

    def __init__(self, ndim, num_features, eps=1e-06, learnable_eps=False):
        """
        Input Variables:
        ----------------
            ndim: An integer indicating the number of dimensions of the expected input tensor.
            num_features: An integer indicating the number of input feature dimensions.
            eps: A scalar constant or learnable variable.
            learnable_eps: A bool value indicating whether the eps is learnable.
        """
        assert ndim in [3, 4, 5
            ], 'FilterResponseNorm only supports 3d, 4d or 5d inputs.'
        super(FilterResponseNormNdNew, self).__init__()
        shape = (1, num_features) + (1,) * (ndim - 2)
        self.eps = nn.Parameter(torch.ones(*shape) * eps)
        if not learnable_eps:
            self.eps.requires_grad_(False)
        self.gamma = nn.Parameter(torch.Tensor(*shape))
        self.beta = nn.Parameter(torch.Tensor(*shape))
        self.tau = nn.Parameter(torch.Tensor(*shape))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.ones_(self.gamma)
        nn.init.zeros_(self.beta)
        nn.init.zeros_(self.tau)

    def forward(self, input_0):
        primals_2 = self.eps
        primals_3 = self.gamma
        primals_4 = self.beta
        primals_5 = self.tau
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
