# 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_6/inductor_cache/kg/ckgosn72xsu5vxglrxam5q6utx6ugdbmjmklsfq42vhqoezat4d3.py
# Topologically Sorted Source Nodes: [output, neg, t, clamp_1, mul, output_1, t_1, clamp_2, mul_1, output_2, t_2, clamp_3, mul_2, output_3, t_3, clamp_4, mul_3, output_4], Original ATen: [aten.clamp, aten.neg, aten.add, aten.mul]
# Source node to ATen node mapping:
#   clamp_1 => clamp_min_1
#   clamp_2 => clamp_min_2
#   clamp_3 => clamp_min_3
#   clamp_4 => clamp_min_4
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   neg => neg
#   output => clamp_min
#   output_1 => add_1
#   output_2 => add_3
#   output_3 => add_5
#   output_4 => add_7
#   t => add
#   t_1 => add_2
#   t_2 => add_4
#   t_3 => add_6
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%primals_1, 0), kwargs = {})
#   %neg : [num_users=4] = call_function[target=torch.ops.aten.neg.default](args = (%primals_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %select), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, %clamp_min_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%clamp_min, %mul), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %select_2), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_2, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_3, %clamp_min_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_1), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %select_4), kwargs = {})
#   %clamp_min_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_4, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_5, %clamp_min_3), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %mul_2), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %select_6), kwargs = {})
#   %clamp_min_4 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_6, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_7, %clamp_min_4), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_5, %mul_3), kwargs = {})
triton_poi_fused_add_clamp_mul_neg_0 = async_compile.triton('triton_poi_fused_add_clamp_mul_neg_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_mul_neg_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_clamp_mul_neg_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = -tmp0
    tmp6 = tmp4 + tmp5
    tmp7 = triton_helpers.maximum(tmp6, tmp1)
    tmp8 = tmp3 * tmp7
    tmp9 = tmp2 + tmp8
    tmp12 = tmp4 + tmp11
    tmp13 = triton_helpers.maximum(tmp12, tmp1)
    tmp14 = tmp10 * tmp13
    tmp15 = tmp9 + tmp14
    tmp18 = tmp4 + tmp17
    tmp19 = triton_helpers.maximum(tmp18, tmp1)
    tmp20 = tmp16 * tmp19
    tmp21 = tmp15 + tmp20
    tmp24 = tmp4 + tmp23
    tmp25 = triton_helpers.maximum(tmp24, tmp1)
    tmp26 = tmp22 * tmp25
    tmp27 = tmp21 + tmp26
    tl.store(out_ptr0 + (x2), tmp27, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output, neg, t, clamp_1, mul, output_1, t_1, clamp_2, mul_1, output_2, t_2, clamp_3, mul_2, output_3, t_3, clamp_4, mul_3, output_4], Original ATen: [aten.clamp, aten.neg, aten.add, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_clamp_mul_neg_0.run(primals_1, primals_3, primals_2, buf0, 256, grid=grid(256), stream=stream0)
    return (buf0, primals_1, primals_2, primals_3, )


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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
from torch import nn
from torch.nn.parameter import Parameter


class APL(nn.Module):
    """
    Implementation of APL (ADAPTIVE PIECEWISE LINEAR UNITS) unit:

        .. math::

            APL(x_i) = max(0,x) + \\sum_{s=1}^{S}{a_i^s * max(0, -x + b_i^s)}

    with trainable parameters a and b, parameter S should be set in advance.

    Shape:
        - Input: (N, *) where * means, any number of additional
          dimensions
        - Output: (N, *), same shape as the input

    Parameters:
        - S: hyperparameter, number of hinges to be set in advance
        - a: trainable parameter, control the slopes of the linear segments
        - b: trainable parameter, determine the locations of the hinges

    References:
        - See APL paper:
        https://arxiv.org/pdf/1412.6830.pdf

    Examples:
        >>> a1 = apl(256, S = 1)
        >>> x = torch.randn(256)
        >>> x = a1(x)
    """

    def __init__(self, in_features, S, a=None, b=None):
        """
        Initialization.
        INPUT:
            - in_features: shape of the input
            - S (int): number of hinges
            - a - value for initialization of parameter, which controls the slopes of the linear segments
            - b - value for initialization of parameter, which determines the locations of the hinges
            a, b are initialized randomly by default
        """
        super(APL, self).__init__()
        self.in_features = in_features
        self.S = S
        if a is None:
            self.a = Parameter(torch.randn((S, in_features), dtype=torch.
                float, requires_grad=True))
        else:
            self.a = a
        if b is None:
            self.b = Parameter(torch.randn((S, in_features), dtype=torch.
                float, requires_grad=True))
        else:
            self.b = b

    def forward(self, x):
        """
        Forward pass of the function
        """
        output = x.clamp(min=0)
        for s in range(self.S):
            t = -x + self.b[s]
            output += self.a[s] * t.clamp(min=0)
        return output


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


def get_init_inputs():
    return [[], {'in_features': 4, 'S': 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 import nn
from torch.nn.parameter import Parameter
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_clamp_mul_neg_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = -tmp0
    tmp6 = tmp4 + tmp5
    tmp7 = triton_helpers.maximum(tmp6, tmp1)
    tmp8 = tmp3 * tmp7
    tmp9 = tmp2 + tmp8
    tmp12 = tmp4 + tmp11
    tmp13 = triton_helpers.maximum(tmp12, tmp1)
    tmp14 = tmp10 * tmp13
    tmp15 = tmp9 + tmp14
    tmp18 = tmp4 + tmp17
    tmp19 = triton_helpers.maximum(tmp18, tmp1)
    tmp20 = tmp16 * tmp19
    tmp21 = tmp15 + tmp20
    tmp24 = tmp4 + tmp23
    tmp25 = triton_helpers.maximum(tmp24, tmp1)
    tmp26 = tmp22 * tmp25
    tmp27 = tmp21 + tmp26
    tl.store(out_ptr0 + x2, tmp27, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_clamp_mul_neg_0[grid(256)](primals_1,
            primals_3, primals_2, buf0, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
    return buf0, primals_1, primals_2, primals_3


class APLNew(nn.Module):
    """
    Implementation of APL (ADAPTIVE PIECEWISE LINEAR UNITS) unit:

        .. math::

            APL(x_i) = max(0,x) + \\sum_{s=1}^{S}{a_i^s * max(0, -x + b_i^s)}

    with trainable parameters a and b, parameter S should be set in advance.

    Shape:
        - Input: (N, *) where * means, any number of additional
          dimensions
        - Output: (N, *), same shape as the input

    Parameters:
        - S: hyperparameter, number of hinges to be set in advance
        - a: trainable parameter, control the slopes of the linear segments
        - b: trainable parameter, determine the locations of the hinges

    References:
        - See APL paper:
        https://arxiv.org/pdf/1412.6830.pdf

    Examples:
        >>> a1 = apl(256, S = 1)
        >>> x = torch.randn(256)
        >>> x = a1(x)
    """

    def __init__(self, in_features, S, a=None, b=None):
        """
        Initialization.
        INPUT:
            - in_features: shape of the input
            - S (int): number of hinges
            - a - value for initialization of parameter, which controls the slopes of the linear segments
            - b - value for initialization of parameter, which determines the locations of the hinges
            a, b are initialized randomly by default
        """
        super(APLNew, self).__init__()
        self.in_features = in_features
        self.S = S
        if a is None:
            self.a = Parameter(torch.randn((S, in_features), dtype=torch.
                float, requires_grad=True))
        else:
            self.a = a
        if b is None:
            self.b = Parameter(torch.randn((S, in_features), dtype=torch.
                float, requires_grad=True))
        else:
            self.b = b

    def forward(self, input_0):
        primals_2 = self.a
        primals_3 = self.b
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
