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]


Measured latency_ms: 0.013813