# 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_7/inductor_cache/gh/cghd2uf4fbc2xvgrsvsqh757dzhgnojtgmv5dt25faylalcrh6ni.py
# Topologically Sorted Source Nodes: [pow_1, nu2, add, denominator, out_1, mul, add_1, sub, relu, out_2], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul, aten.sub, aten.relu]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   denominator => sqrt
#   mul => mul
#   nu2 => mean
#   out_1 => div
#   out_2 => add_2
#   pow_1 => pow_1
#   relu => relu
#   sub => sub
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [-1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 1e-16), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %view_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %div), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %view_3), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, %view_4), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, %view_4), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_relu_sqrt_sub_0 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_relu_sqrt_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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_pow_relu_sqrt_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_add_div_mean_mul_pow_relu_sqrt_sub_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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)
    tmp11 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + (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
    tmp8 = 1e-16
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.sqrt(tmp9)
    tmp12 = tmp0 / tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full([1, 1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tmp20 = tmp19 + tmp16
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp10, xmask)
    tl.store(out_ptr0 + (r1 + (16*x0)), tmp20, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, nu2, add, denominator, out_1, mul, add_1, sub, relu, out_2], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul, aten.sub, aten.relu]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_pow_relu_sqrt_sub_0.run(buf1, primals_1, primals_2, primals_3, primals_4, buf2, 16, 16, grid=grid(16), stream=stream0)
    return (buf2, primals_1, primals_2, primals_3, primals_4, reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0), )


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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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.nn.functional as func
import torch.jit
import torch.nn


class FilterResponseNorm(nn.Module):

    def __init__(self, in_size, eps=1e-16):
        super().__init__()
        self.eps = eps
        self.in_size = in_size
        self.register_parameter('scale', nn.Parameter(torch.ones(in_size,
            dtype=torch.float)))
        self.register_parameter('bias', nn.Parameter(torch.zeros(in_size,
            dtype=torch.float)))
        self.register_parameter('threshold', nn.Parameter(torch.zeros(
            in_size, dtype=torch.float)))

    def forward(self, inputs):
        out = inputs.view(inputs.size(0), inputs.size(1), -1)
        nu2 = (out ** 2).mean(dim=-1)
        extension = [1] * (inputs.dim() - 2)
        denominator = torch.sqrt(nu2 + self.eps)
        denominator = denominator.view(inputs.size(0), inputs.size(1), *
            extension)
        scale = self.scale.view(1, self.scale.size(0), *extension)
        bias = self.bias.view(1, self.bias.size(0), *extension)
        threshold = self.threshold.view(1, self.threshold.size(0), *extension)
        out = inputs / denominator.detach()
        out = func.relu(scale * out + bias - threshold) + threshold
        return out


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


def get_init_inputs():
    return [[], {'in_size': 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
import torch.nn as nn
import torch.jit
import torch.nn
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_add_div_mean_mul_pow_relu_sqrt_sub_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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)
    tmp11 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + 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
    tmp8 = 1e-16
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.sqrt(tmp9)
    tmp12 = tmp0 / tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full([1, 1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tmp20 = tmp19 + tmp16
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp20, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_pow_relu_sqrt_sub_0[grid(16)](buf1,
            primals_1, primals_2, primals_3, primals_4, buf2, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
    return (buf2, primals_1, primals_2, primals_3, primals_4,
        reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0))


class FilterResponseNormNew(nn.Module):

    def __init__(self, in_size, eps=1e-16):
        super().__init__()
        self.eps = eps
        self.in_size = in_size
        self.register_parameter('scale', nn.Parameter(torch.ones(in_size,
            dtype=torch.float)))
        self.register_parameter('bias', nn.Parameter(torch.zeros(in_size,
            dtype=torch.float)))
        self.register_parameter('threshold', nn.Parameter(torch.zeros(
            in_size, dtype=torch.float)))

    def forward(self, input_0):
        primals_2 = self.scale
        primals_3 = self.bias
        primals_4 = self.threshold
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
