# 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/q2/cq2qrtyf6mtvmdrqgmyex3qru5qkrvvs4wppvbxy3li4uxc3exl4.py
# Topologically Sorted Source Nodes: [std, mul, abs_1, m, mul_1, alpha_w], Original ATen: [aten.std, aten.mul, aten.abs, aten.mean, aten.sub]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   alpha_w => sub
#   m => mean
#   mul => mul
#   mul_1 => mul_1
#   std => sqrt, var
# Graph fragment:
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%arg0_1,), kwargs = {correction: 1.0})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, 12.987012987012987), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 13.155844155844155), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_per_fused_abs_mean_mul_std_sub_0 = async_compile.triton('triton_per_fused_abs_mean_mul_std_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=[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_mean_mul_std_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 4, '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_mean_mul_std_sub_0(in_out_ptr0, in_ptr0, 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 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = tl_math.abs(tmp0)
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
    tmp18 = 255.0
    tmp19 = tmp13 / tmp18
    tmp20 = libdevice.sqrt(tmp19)
    tmp21 = 12.987012987012987
    tmp22 = tmp20 * tmp21
    tmp23 = 256.0
    tmp24 = tmp17 / tmp23
    tmp25 = 13.155844155844155
    tmp26 = tmp24 * tmp25
    tmp27 = tmp22 - tmp26
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp27, 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((), (), torch.float32)
        buf4 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [std, mul, abs_1, m, mul_1, alpha_w], Original ATen: [aten.std, aten.mul, aten.abs, aten.mean, aten.sub]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_mean_mul_std_sub_0.run(buf4, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf4, )


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


def stats_quant(x, nbit, qmode='symm', dequantize=True):
    z_typical = {'4bit': [0.077, 1.013], '8bit': [0.027, 1.114]}
    z = z_typical[f'{int(nbit)}bit']
    m = x.abs().mean()
    std = x.std()
    if qmode == 'symm':
        n_lv = 2 ** (nbit - 1) - 1
        alpha_w = 1 / z[0] * std - z[1] / z[0] * m
    elif qmode == 'asymm':
        n_lv = (2 ** nbit - 1) / 2
        alpha_w = 2 * m
    else:
        raise NotImplementedError
    x = x.clamp(-alpha_w.item(), alpha_w.item())
    scale = n_lv / alpha_w
    xq = x.mul(scale).round()
    if len(xq.unique()) > 2 ** nbit:
        xq = xq.clamp(-2 ** nbit // 2, 2 ** nbit // 2 - 1)
    if dequantize:
        xq = xq.div(scale)
    return xq, scale


class RoundQ(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input, wbit, qmode):
        input_q, _scale = stats_quant(input, wbit, qmode)
        ctx.save_for_backward(input)
        return input_q

    @staticmethod
    def backward(ctx, grad_output):
        grad_input = grad_output.clone()
        return grad_input, None, None


class WQ(nn.Module):
    """
    Weight quantizer
    """

    def __init__(self, wbit, qmode='symm'):
        super(WQ, self).__init__()
        self.wbit = wbit
        self.qmode = qmode

    def forward(self, x):
        weight_q = RoundQ.apply(x, self.wbit, self.qmode)
        return weight_q

    def extra_repr(self):
        return super(WQ, self).extra_repr() + 'qmode={}'.format(self.qmode)


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


def get_init_inputs():
    return [[], {'wbit': 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 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_mean_mul_std_sub_0(in_out_ptr0, in_ptr0, 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 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = tl_math.abs(tmp0)
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
    tmp18 = 255.0
    tmp19 = tmp13 / tmp18
    tmp20 = libdevice.sqrt(tmp19)
    tmp21 = 12.987012987012987
    tmp22 = tmp20 * tmp21
    tmp23 = 256.0
    tmp24 = tmp17 / tmp23
    tmp25 = 13.155844155844155
    tmp26 = tmp24 * tmp25
    tmp27 = tmp22 - tmp26
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp27, 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((), (), torch.float32)
        buf4 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_abs_mean_mul_std_sub_0[grid(1)](buf4, arg0_1, 1, 
            256, num_warps=2, num_stages=1)
        del arg0_1
    return buf4,


def stats_quant(x, nbit, qmode='symm', dequantize=True):
    z_typical = {'4bit': [0.077, 1.013], '8bit': [0.027, 1.114]}
    z = z_typical[f'{int(nbit)}bit']
    m = x.abs().mean()
    std = x.std()
    if qmode == 'symm':
        n_lv = 2 ** (nbit - 1) - 1
        alpha_w = 1 / z[0] * std - z[1] / z[0] * m
    elif qmode == 'asymm':
        n_lv = (2 ** nbit - 1) / 2
        alpha_w = 2 * m
    else:
        raise NotImplementedError
    x = x.clamp(-alpha_w.item(), alpha_w.item())
    scale = n_lv / alpha_w
    xq = x.mul(scale).round()
    if len(xq.unique()) > 2 ** nbit:
        xq = xq.clamp(-2 ** nbit // 2, 2 ** nbit // 2 - 1)
    if dequantize:
        xq = xq.div(scale)
    return xq, scale


class RoundQ(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input, wbit, qmode):
        input_q, _scale = stats_quant(input, wbit, qmode)
        ctx.save_for_backward(input)
        return input_q

    @staticmethod
    def backward(ctx, grad_output):
        grad_input = grad_output.clone()
        return grad_input, None, None


class WQNew(nn.Module):
    """
    Weight quantizer
    """

    def __init__(self, wbit, qmode='symm'):
        super(WQNew, self).__init__()
        self.wbit = wbit
        self.qmode = qmode

    def extra_repr(self):
        return super(WQNew, self).extra_repr() + 'qmode={}'.format(self.qmode)

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