# 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/xx/cxx3k6yhlxj6nn7gswfjvdbi6pmw3hzhig5r6lpxacbee7gy4kvj.py
# Topologically Sorted Source Nodes: [output, truediv, abs_1, max_1, truediv_1, output_1, truediv_2, output_2, output_3, mul_1, output_4], Original ATen: [aten.tanh, aten.div, aten.abs, aten.max, aten.add, aten.round, aten.mul, aten.sub]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   max_1 => max_1
#   mul_1 => mul_1
#   output => tanh
#   output_1 => add
#   output_2 => round_1
#   output_3 => mul
#   output_4 => sub
#   truediv => div
#   truediv_1 => div_1
#   truediv_2 => div_2
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%arg0_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%tanh, 2), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%tanh,), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.default](args = (%abs_1,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div, %max_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_1, 0.5), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, 0.06666666666666667), kwargs = {})
#   %round_1 : [num_users=1] = call_function[target=torch.ops.aten.round.default](args = (%div_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%round_1, 0.06666666666666667), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, 1), kwargs = {})
triton_per_fused_abs_add_div_max_mul_round_sub_tanh_0 = async_compile.triton('triton_per_fused_abs_add_div_max_mul_round_sub_tanh_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=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_div_max_mul_round_sub_tanh_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 1, '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_div_max_mul_round_sub_tanh_0(in_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = tl_math.abs(tmp1)
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tmp6 = 0.5
    tmp7 = tmp1 * tmp6
    tmp8 = tmp7 / tmp5
    tmp9 = tmp8 + tmp6
    tmp10 = 15.0
    tmp11 = tmp9 * tmp10
    tmp12 = libdevice.nearbyint(tmp11)
    tmp13 = 0.06666666666666667
    tmp14 = tmp12 * tmp13
    tmp15 = 2.0
    tmp16 = tmp14 * tmp15
    tmp17 = 1.0
    tmp18 = tmp16 - tmp17
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [RBLOCK])), tmp18, None)
''', 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output, truediv, abs_1, max_1, truediv_1, output_1, truediv_2, output_2, output_3, mul_1, output_4], Original ATen: [aten.tanh, aten.div, aten.abs, aten.max, aten.add, aten.round, aten.mul, aten.sub]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_div_max_mul_round_sub_tanh_0.run(arg0_1, buf1, 1, 256, grid=grid(1), 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)

from torch.autograd import Function
import torch
import torch.nn as nn


class Round(Function):

    @staticmethod
    def forward(self, input):
        output = torch.round(input)
        return output

    @staticmethod
    def backward(self, grad_output):
        grad_input = grad_output.clone()
        return grad_input


class WeightQuantizer(nn.Module):

    def __init__(self, w_bits):
        super(WeightQuantizer, self).__init__()
        self.w_bits = w_bits

    def round(self, input):
        output = Round.apply(input)
        return output

    def forward(self, input):
        if self.w_bits == 32:
            output = input
        elif self.w_bits == 1:
            None
            assert self.w_bits != 1
        else:
            output = torch.tanh(input)
            output = output / 2 / torch.max(torch.abs(output)) + 0.5
            scale = 1 / float(2 ** self.w_bits - 1)
            output = self.round(output / scale) * scale
            output = 2 * output - 1
        return output


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


def get_init_inputs():
    return [[], {'w_bits': 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
from torch.autograd import Function
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_per_fused_abs_add_div_max_mul_round_sub_tanh_0(in_ptr0, out_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = tl_math.abs(tmp1)
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tmp6 = 0.5
    tmp7 = tmp1 * tmp6
    tmp8 = tmp7 / tmp5
    tmp9 = tmp8 + tmp6
    tmp10 = 15.0
    tmp11 = tmp9 * tmp10
    tmp12 = libdevice.nearbyint(tmp11)
    tmp13 = 0.06666666666666667
    tmp14 = tmp12 * tmp13
    tmp15 = 2.0
    tmp16 = tmp14 * tmp15
    tmp17 = 1.0
    tmp18 = tmp16 - tmp17
    tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp18, None)


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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_add_div_max_mul_round_sub_tanh_0[grid(1)](arg0_1,
            buf1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
    return buf1,


class Round(Function):

    @staticmethod
    def forward(self, input):
        output = torch.round(input)
        return output

    @staticmethod
    def backward(self, grad_output):
        grad_input = grad_output.clone()
        return grad_input


class WeightQuantizerNew(nn.Module):

    def __init__(self, w_bits):
        super(WeightQuantizerNew, self).__init__()
        self.w_bits = w_bits

    def round(self, input):
        output = Round.apply(input)
        return output

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