# 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/bl/cbl4itgejq75azdj3ac7grz4cozr5g7zmozwin2n4qjqbsoehuwj.py
# Topologically Sorted Source Nodes: [out_forward, mask1, type_1, mul, mul_1, mul_2, add, type_2, sub, mul_3, out1, mask2, type_3, mul_4, neg, mul_5, mul_6, add_2, type_4, sub_1, mul_7, out2, mask3, type_5, mul_8, type_6, sub_2, mul_9, out3, sub_3, out], Original ATen: [aten.sign, aten.lt, aten._to_copy, aten.mul, aten.add, aten.rsub, aten.neg, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   add_2 => add_2
#   mask1 => lt
#   mask2 => lt_1
#   mask3 => lt_2
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_6 => mul_6
#   mul_7 => mul_7
#   mul_8 => mul_8
#   mul_9 => mul_9
#   neg => neg
#   out => add_5
#   out1 => add_1
#   out2 => add_3
#   out3 => add_4
#   out_forward => sign
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   type_1 => convert_element_type
#   type_2 => convert_element_type_1
#   type_3 => convert_element_type_2
#   type_4 => convert_element_type_3
#   type_5 => convert_element_type_4
#   type_6 => convert_element_type_5
# Graph fragment:
#   %sign : [num_users=1] = call_function[target=torch.ops.aten.sign.default](args = (%arg0_1,), kwargs = {})
#   %lt : [num_users=2] = call_function[target=torch.ops.aten.lt.Scalar](args = (%arg0_1, -1), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt, torch.float32), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, -1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %arg0_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt, torch.float32), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %convert_element_type_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %sub), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_3), kwargs = {})
#   %lt_1 : [num_users=2] = call_function[target=torch.ops.aten.lt.Scalar](args = (%arg0_1, 0), kwargs = {})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt_1, torch.float32), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, %convert_element_type_2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %arg0_1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 2), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %mul_6), kwargs = {})
#   %convert_element_type_3 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt_1, torch.float32), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %convert_element_type_3), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, %sub_1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_7), kwargs = {})
#   %lt_2 : [num_users=2] = call_function[target=torch.ops.aten.lt.Scalar](args = (%arg0_1, 1), kwargs = {})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt_2, torch.float32), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, %convert_element_type_4), kwargs = {})
#   %convert_element_type_5 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt_2, torch.float32), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %convert_element_type_5), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, 1), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, %mul_9), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sign, %add_4), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_3, %add_4), kwargs = {})
triton_poi_fused__to_copy_add_lt_mul_neg_rsub_sign_sub_0 = async_compile.triton('triton_poi_fused__to_copy_add_lt_mul_neg_rsub_sign_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_lt_mul_neg_rsub_sign_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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__to_copy_add_lt_mul_neg_rsub_sign_sub_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = -1.0
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp10 * tmp8
    tmp12 = tmp0 * tmp0
    tmp13 = 2.0
    tmp14 = tmp0 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = 1.0
    tmp17 = tmp16 - tmp10
    tmp18 = tmp15 * tmp17
    tmp19 = tmp11 + tmp18
    tmp20 = 0.0
    tmp21 = tmp0 < tmp20
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 * tmp22
    tmp24 = -tmp0
    tmp25 = tmp24 * tmp0
    tmp26 = tmp25 + tmp14
    tmp27 = tmp16 - tmp22
    tmp28 = tmp26 * tmp27
    tmp29 = tmp23 + tmp28
    tmp30 = tmp0 < tmp16
    tmp31 = tmp30.to(tl.float32)
    tmp32 = tmp29 * tmp31
    tmp33 = tmp16 - tmp31
    tmp34 = tmp33 * tmp16
    tmp35 = tmp32 + tmp34
    tmp36 = tmp7 - tmp35
    tmp37 = tmp36 + tmp35
    tl.store(in_out_ptr0 + (x0), tmp37, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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, 4), (64, 16, 4, 1), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [out_forward, mask1, type_1, mul, mul_1, mul_2, add, type_2, sub, mul_3, out1, mask2, type_3, mul_4, neg, mul_5, mul_6, add_2, type_4, sub_1, mul_7, out2, mask3, type_5, mul_8, type_6, sub_2, mul_9, out3, sub_3, out], Original ATen: [aten.sign, aten.lt, aten._to_copy, aten.mul, aten.add, aten.rsub, aten.neg, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_add_lt_mul_neg_rsub_sign_sub_0.run(buf1, arg0_1, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf1, )


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)
    fn = lambda: call([arg0_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
import torch.utils
import torch.utils.data.distributed


class BinaryActivation(nn.Module):

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

    def forward(self, x):
        out_forward = torch.sign(x)
        mask1 = x < -1
        mask2 = x < 0
        mask3 = x < 1
        out1 = -1 * mask1.type(torch.float32) + (x * x + 2 * x) * (1 -
            mask1.type(torch.float32))
        out2 = out1 * mask2.type(torch.float32) + (-x * x + 2 * x) * (1 -
            mask2.type(torch.float32))
        out3 = out2 * mask3.type(torch.float32) + 1 * (1 - mask3.type(torch
            .float32))
        out = out_forward.detach() - out3.detach() + out3
        return out


def get_inputs():
    return [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
import torch.nn as nn
import torch.utils
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


@triton.jit
def triton_poi_fused__to_copy_add_lt_mul_neg_rsub_sign_sub_0(in_out_ptr0,
    in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = -1.0
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp10 * tmp8
    tmp12 = tmp0 * tmp0
    tmp13 = 2.0
    tmp14 = tmp0 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = 1.0
    tmp17 = tmp16 - tmp10
    tmp18 = tmp15 * tmp17
    tmp19 = tmp11 + tmp18
    tmp20 = 0.0
    tmp21 = tmp0 < tmp20
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 * tmp22
    tmp24 = -tmp0
    tmp25 = tmp24 * tmp0
    tmp26 = tmp25 + tmp14
    tmp27 = tmp16 - tmp22
    tmp28 = tmp26 * tmp27
    tmp29 = tmp23 + tmp28
    tmp30 = tmp0 < tmp16
    tmp31 = tmp30.to(tl.float32)
    tmp32 = tmp29 * tmp31
    tmp33 = tmp16 - tmp31
    tmp34 = tmp33 * tmp16
    tmp35 = tmp32 + tmp34
    tmp36 = tmp7 - tmp35
    tmp37 = tmp36 + tmp35
    tl.store(in_out_ptr0 + x0, tmp37, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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, 4), (64, 16, 4, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused__to_copy_add_lt_mul_neg_rsub_sign_sub_0[grid(256)](
            buf1, arg0_1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf1,


class BinaryActivationNew(nn.Module):

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

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