# 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_1/inductor_cache/nt/cnt5xwsci4q4h3cvlzzh66flvlit77lerlwrimpneefxep4t5ytk.py
# Topologically Sorted Source Nodes: [abs_1, max_1, truediv, x, div_, round_, mul_, cos_, mul__1], Original ATen: [aten.abs, aten.max, aten.div, aten.acos, aten.round, aten.mul, aten.cos]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   cos_ => cos
#   div_ => div_1
#   max_1 => max_1
#   mul_ => mul
#   mul__1 => mul_1
#   round_ => round_1
#   truediv => div
#   x => acos
# Graph fragment:
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%abs_1, -1, True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %getitem), kwargs = {})
#   %acos : [num_users=1] = call_function[target=torch.ops.aten.acos.default](args = (%div,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%acos, 0.20943951023931953), kwargs = {})
#   %round_1 : [num_users=1] = call_function[target=torch.ops.aten.round.default](args = (%div_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%round_1, 0.20943951023931953), kwargs = {})
#   %cos : [num_users=1] = call_function[target=torch.ops.aten.cos.default](args = (%mul,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cos, %getitem), kwargs = {})
triton_poi_fused_abs_acos_cos_div_max_mul_round_0 = async_compile.triton('triton_poi_fused_abs_acos_cos_div_max_mul_round_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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_acos_cos_div_max_mul_round_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_abs_acos_cos_div_max_mul_round_0(in_ptr0, 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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.abs(tmp1)
    tmp4 = tl_math.abs(tmp3)
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = tl_math.abs(tmp6)
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tmp10 = tl_math.abs(tmp9)
    tmp11 = triton_helpers.maximum(tmp8, tmp10)
    tmp12 = tmp0 / tmp11
    tmp13 = libdevice.acos(tmp12)
    tmp14 = 4.7746482927568605
    tmp15 = tmp13 * tmp14
    tmp16 = libdevice.nearbyint(tmp15)
    tmp17 = 0.20943951023931953
    tmp18 = tmp16 * tmp17
    tmp19 = tl_math.cos(tmp18)
    tmp20 = tmp19 * tmp11
    tl.store(out_ptr0 + (x2), tmp20, 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)
        # Topologically Sorted Source Nodes: [abs_1, max_1, truediv, x, div_, round_, mul_, cos_, mul__1], Original ATen: [aten.abs, aten.max, aten.div, aten.acos, aten.round, aten.mul, aten.cos]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_acos_cos_div_max_mul_round_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf0, )


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 numpy as np
import torch.cuda
import torch.fft


def diagonal_quantize_function(x, bit, phase_noise_std=0, random_state=None,
    gradient_clip=False):


    class DiagonalQuantizeFunction(torch.autograd.Function):

        @staticmethod
        def forward(ctx, x):
            S_scale = x.abs().max(dim=-1, keepdim=True)[0]
            x = (x / S_scale).acos()
            ratio = np.pi / (2 ** bit - 1)
            x.div_(ratio).round_().mul_(ratio)
            if phase_noise_std > 1e-05:
                noise = gen_gaussian_noise(x, noise_mean=0, noise_std=
                    phase_noise_std, trunc_range=[-2 * phase_noise_std, 2 *
                    phase_noise_std], random_state=random_state)
                x.add_(noise)
            x.cos_().mul_(S_scale)
            return x

        @staticmethod
        def backward(ctx, grad_output):
            if gradient_clip:
                grad_input = grad_output.clamp(-1, 1)
            else:
                grad_input = grad_output.clone()
            return grad_input
    return DiagonalQuantizeFunction.apply(x)


class DiagonalQuantizer(torch.nn.Module):

    def __init__(self, bit, phase_noise_std=0.0, random_state=None, device=
        torch.device('cuda')):
        """2021/02/18: New phase quantizer for Sigma matrix in MZI-ONN. Gaussian phase noise is supported. All singular values are normalized by a TIA gain (S_scale), the normalized singular values will be achieved by cos(phi), phi will have [0, pi] uniform quantization.
        We do not consider real MZI implementation, thus voltage quantization and gamma noises are not supported.
        Args:
            bit (int): bitwidth for phase quantization.
            phase_noise_std (float, optional): Std dev for Gaussian phase noises. Defaults to 0.
            random_state (int, optional): random_state to control random noise injection. Defaults to None.
            device (torch.Device, optional): torch.Device. Defaults to torch.device("cuda").
        """
        super().__init__()
        self.bit = bit
        self.phase_noise_std = phase_noise_std
        self.random_state = random_state
        self.device = device

    def set_phase_noise_std(self, phase_noise_std=0, random_state=None):
        self.phase_noise_std = phase_noise_std
        self.random_state = random_state

    def forward(self, x):
        x = diagonal_quantize_function(x, self.bit, self.phase_noise_std,
            self.random_state, gradient_clip=True)
        return x


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


def get_init_inputs():
    return [[], {'bit': 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 numpy as np
import torch.cuda
import torch.fft
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_abs_acos_cos_div_max_mul_round_0(in_ptr0, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.abs(tmp1)
    tmp4 = tl_math.abs(tmp3)
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = tl_math.abs(tmp6)
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tmp10 = tl_math.abs(tmp9)
    tmp11 = triton_helpers.maximum(tmp8, tmp10)
    tmp12 = tmp0 / tmp11
    tmp13 = libdevice.acos(tmp12)
    tmp14 = 4.7746482927568605
    tmp15 = tmp13 * tmp14
    tmp16 = libdevice.nearbyint(tmp15)
    tmp17 = 0.20943951023931953
    tmp18 = tmp16 * tmp17
    tmp19 = tl_math.cos(tmp18)
    tmp20 = tmp19 * tmp11
    tl.store(out_ptr0 + x2, tmp20, 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)
        get_raw_stream(0)
        triton_poi_fused_abs_acos_cos_div_max_mul_round_0[grid(256)](arg0_1,
            buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def diagonal_quantize_function(x, bit, phase_noise_std=0, random_state=None,
    gradient_clip=False):

    class DiagonalQuantizeFunction(torch.autograd.Function):

        @staticmethod
        def forward(ctx, x):
            S_scale = x.abs().max(dim=-1, keepdim=True)[0]
            x = (x / S_scale).acos()
            ratio = np.pi / (2 ** bit - 1)
            x.div_(ratio).round_().mul_(ratio)
            if phase_noise_std > 1e-05:
                noise = gen_gaussian_noise(x, noise_mean=0, noise_std=
                    phase_noise_std, trunc_range=[-2 * phase_noise_std, 2 *
                    phase_noise_std], random_state=random_state)
                x.add_(noise)
            x.cos_().mul_(S_scale)
            return x

        @staticmethod
        def backward(ctx, grad_output):
            if gradient_clip:
                grad_input = grad_output.clamp(-1, 1)
            else:
                grad_input = grad_output.clone()
            return grad_input
    return DiagonalQuantizeFunction.apply(x)


class DiagonalQuantizerNew(torch.nn.Module):

    def __init__(self, bit, phase_noise_std=0.0, random_state=None, device=
        torch.device('cuda')):
        """2021/02/18: New phase quantizer for Sigma matrix in MZI-ONN. Gaussian phase noise is supported. All singular values are normalized by a TIA gain (S_scale), the normalized singular values will be achieved by cos(phi), phi will have [0, pi] uniform quantization.
        We do not consider real MZI implementation, thus voltage quantization and gamma noises are not supported.
        Args:
            bit (int): bitwidth for phase quantization.
            phase_noise_std (float, optional): Std dev for Gaussian phase noises. Defaults to 0.
            random_state (int, optional): random_state to control random noise injection. Defaults to None.
            device (torch.Device, optional): torch.Device. Defaults to torch.device("cuda").
        """
        super().__init__()
        self.bit = bit
        self.phase_noise_std = phase_noise_std
        self.random_state = random_state
        self.device = device

    def set_phase_noise_std(self, phase_noise_std=0, random_state=None):
        self.phase_noise_std = phase_noise_std
        self.random_state = random_state

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