LWC 49:673. Number of Longest Increasing Subsequence

本文介绍了如何解决LeetCode上的673题——求解最长递增子序列的数量问题。通过动态规划的方法,定义状态cnt[i]表示达到最长递增子序列所需的边数,从而找出所有可能的最长递增子序列数量。

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LWC 49:673. Number of Longest Increasing Subsequence

传送门:673. Number of Longest Increasing Subsequence

Problem:

Given an unsorted array of integers, find the number of longest increasing subsequence.

Example 1:

Input: [1,3,5,4,7]
Output: 2
Explanation: The two longest increasing subsequence are [1, 3, 4, 7] and [1, 3, 5, 7].

Example 2:

Input: [2,2,2,2,2]
Output: 5
Explanation: The length of longest continuous increasing subsequence is 1, and there are 5 subsequences’ length is 1, so output 5.

Note:

Length of the given array will be not exceed 2000 and the answer is guaranteed to be fit in 32-bit signed int.

题意:最长递增子序列的升级版,求最大长度下,总共有多少条路径。

比如:

1 2 4 3 5 4 7 2

1 2   3 5   7
1 2 4   5   7
1 2   3   4 7

所以总共有三条,抽象成结点和边的关系为:
1 -> 2 -> 4 -> 5 -> 7
       -> 3 -> 5 
            -> 4 -> 7

从横切面来看,无非就是求解结点从一阶段转移到下一阶段的最大边数。

所以可以定义状态cnt[i]表示,当且位置下,达到最长递增子序列所需的边数

if (如果下一阶段再一次取得最长递增长度) 说明有新的路径产生
    cnt[i] += cnt[j]  j < i
if (如果第一次更新最长递增长度) 之前累加的cnt[i]都必须清零
    cnt[i] = cnt[j]   j < i

代码如下:

    public int findNumberOfLIS(int[] nums) {
        int n = nums.length, max_len = 0;
        int[] len = new int[n];
        int[] cnt = new int[n];
        for (int i = 0; i < n; ++i) {
            len[i] = 1;
            cnt[i] = 1;
            for (int j = 0; j < i; ++j) {
                if (nums[i] > nums[j]) {
                    if (len[i] == len[j] + 1) cnt[i] += cnt[j];
                    else if (len[i] < len[j] + 1) {
                        len[i] = len[j] + 1;
                        cnt[i] = cnt[j];
                    }
                }
                max_len = Math.max(max_len, len[i]);
            }
        }

        int res = 0;
        for (int i = 0; i < n; ++i) {
            if (len[i] == max_len) res += cnt[i];
        }
        return res;
    }
class UniformAffineQuantizer(nn.Module): def __init__( self, n_bits: int = 8, symmetric: bool = False, per_channel_axes=[], metric="minmax", dynamic=False, dynamic_method="per_cluster", group_size=None, shape=None, lwc=False, disable_zero_point=False, ): """ support cluster quantize dynamic_method support per_token and per_cluster """ super().__init__() self.symmetric = symmetric self.disable_zero_point = disable_zero_point assert 2 <= n_bits <= 16, "bitwidth not supported" self.n_bits = n_bits if self.disable_zero_point: self.qmin = -(2 ** (n_bits - 1)) self.qmax = 2 ** (n_bits - 1) - 1 else: self.qmin = 0 self.qmax = 2 ** (n_bits) - 1 self.per_channel_axes = per_channel_axes self.metric = metric self.cluster_counts = None self.cluster_dim = None self.scale = None self.zero_point = None self.round_zero_point = None self.cached_xmin = None self.cached_xmax = None self.dynamic = dynamic self.dynamic_method = dynamic_method self.deficiency = 0 self.lwc = lwc init_value = 4. # inti value of learnable weight clipping if lwc: if group_size: dim1 = int(shape[0]*math.ceil(shape[1]/group_size)) self.deficiency = shape[-1]%group_size if self.deficiency > 0: self.deficiency = group_size - self.deficiency assert self.symmetric # support for mlc-llm symmetric quantization else: dim1 = shape[0] self.upbound_factor = nn.Parameter(torch.ones((dim1,1))*init_value) self.lowbound_factor = nn.Parameter(torch.ones((dim1,1))*init_value) self.sigmoid = nn.Sigmoid() self.enable = True self.group_size = group_size def change_n_bits(self, n_bits): self.n_bits = n_bits if self.disable_zero_point: self.qmin = -(2 ** (n_bits - 1)) self.qmax = 2 ** (n_bits - 1) - 1 else: self.qmin = 0 self.qmax = 2 ** (n_bits) - 1 def fake_quant(self, x, scale, round_zero_point): if self.deficiency > 0: pad_zeros = torch.zeros((x.shape[0],self.deficiency),dtype=x.dtype,device=x.device) x = torch.cat((x,pad_zeros),dim=1) if self.group_size: assert len(x.shape)==2, "only support linear layer now" dim1, dim2 = x.shape x = x.reshape(-1, self.group_size) x_int = round_ste(x / scale) if round_zero_point is not None: x_int = x_int.add(round_zero_point) x_int = x_int.clamp(self.qmin, self.qmax) x_dequant = x_int if round_zero_point is not None: x_dequant = x_dequant.sub(round_zero_point) x_dequant = x_dequant.mul(scale) if self.group_size: x_dequant = x_dequant.reshape(dim1, dim2) if self.deficiency > 0: x_dequant = x_dequant[:,:-self.deficiency] return x_dequant def forward(self, x: torch.Tensor): if self.n_bits >= 16 or not self.enable: return x if self.metric == "fix0to1": return x.mul_(2**self.n_bits-1).round_().div_(2**self.n_bits-1) if self.dynamic_method == "per_token" or self.dynamic_method == "per_channel": self.per_token_dynamic_calibration(x) else: raise NotImplementedError() x_dequant = self.fake_quant(x, self.scale, self.round_zero_point) return x_dequant def per_token_dynamic_calibration(self, x): if self.group_size: if self.deficiency == 0: x = x.reshape(-1,self.group_size) else: pad_zeros = torch.zeros((x.shape[0],self.deficiency),dtype=x.dtype,device=x.device) x = torch.cat((x,pad_zeros),dim=1) x = x.reshape(-1,self.group_size) reduce_shape = [-1] xmin = x.amin(reduce_shape, keepdim=True) xmax = x.amax(reduce_shape, keepdim=True) if self.lwc: xmax = self.sigmoid(self.upbound_factor)*xmax xmin = self.sigmoid(self.lowbound_factor)*xmin if self.symmetric: abs_max = torch.max(xmax.abs(),xmin.abs()) scale = abs_max / (2**(self.n_bits-1)-1) self.scale = scale.clamp(min=CLIPMIN, max=1e4) zero_point = (2**(self.n_bits-1)-1)*torch.ones_like(self.scale) else: range = xmax - xmin scale = range / (2**self.n_bits-1) self.scale = scale.clamp(min=CLIPMIN, max=1e4) zero_point = -(xmin) / (self.scale) if self.disable_zero_point: self.round_zero_point = None else: self.round_zero_point = zero_point.clamp(min=-1e4, max=1e4).round() def register_scales_and_zeros(self): self.register_buffer('scales', self.scale) self.register_buffer('zeros', self.round_zero_point) del self.scale del self.round_zero_point
最新发布
07-24
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