LWC 65: 756. Pyramid Transition Matrix

本文探讨了如何使用动态规划解决金字塔构造问题。通过分析已知的底部元素和允许的三元组,确定是否能成功构建到金字塔顶部。介绍了算法的实现思路,并提供了Java和Python两种语言的代码实现。

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LWC 65: 756. Pyramid Transition Matrix

传送门:Pyramid Transition Matrix

Problem:

We are stacking blocks to form a pyramid. Each block has a color which is a one letter string, like 'Z'.

For every block of color C we place not in the bottom row, we are placing it on top of a left block of color A and right block of color B. We are allowed to place the block there only if (A, B, C) is an allowed triple.

We start with a bottom row of bottom, represented as a single string. We also start with a list of allowed triples allowed. Each allowed triple is represented as a string of length 3.

Return true if we can build the pyramid all the way to the top, otherwise false.

Example 1:

Input: bottom = “XYZ”, allowed = [“XYD”, “YZE”, “DEA”, “FFF”]
Output: true
Explanation:
We can stack the pyramid like this:
A
/ \
D E
/ \ / \
X Y Z

This works because (‘X’, ‘Y’, ‘D’), (‘Y’, ‘Z’, ‘E’), and (‘D’, ‘E’, ‘A’) are allowed triples.

Example 1:

Input: bottom = “XXYX”, allowed = [“XXX”, “XXY”, “XYX”, “XYY”, “YXZ”]
Output: false
Explanation:
We can’t stack the pyramid to the top.
Note that there could be allowed triples (A, B, C) and (A, B, D) with C != D.

Note:

  • bottom will be a string with length in range [2, 12].
  • allowed will have length in range [0, 343].
  • Letters in all strings will be chosen from the set {‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’}.

思路:
注意allowed中的三元组是可以重复利用的,这样我们定义dp:

dp[i][j][k]: 表示第i层上,第j个元素为k

根据bottom,可以初始化每个位置j上含有的字符bottom[j], dp更新式如下:

dp[i][j][k] = true if dp[i + 1][j][l] = true && dp[i + 1][j + 1][r] = true && lrk组成的字符串在allowed中出现过。

Java版本:

    public boolean pyramidTransition(String bottom, List<String> allowed) {
        Map<String, List<String>> mem = new HashMap<>();
        boolean[][] dp = new boolean[20][7];
        int n = bottom.length();

        for (String allow : allowed) {
            mem.computeIfAbsent(allow.substring(0, 2), k -> new ArrayList<>()).add(allow.substring(2));
        }

        for (int i = 0; i < n; ++i) {
            dp[i][bottom.charAt(i) - 'A'] = true;
        }

        for (int i = n - 1; i >= 1; --i) {
            boolean[][] ndp = new boolean[20][7];
            for (int j = 0; j < i; ++j) {
                for (int l = 0; l < 7; ++l) {
                    for (int r = 0; r < 7; ++r) {
                        if (dp[j][l] && dp[j + 1][r]) {
                            if (mem.containsKey((char)(l + 'A') + "" + (char)(r + 'A'))) {
                                for (String s : mem.get((char)(l + 'A') + "" + (char)(r + 'A'))) {
                                    ndp[j][s.charAt(0) - 'A'] = true;
                                }
                            }
                        }
                    }
                }
            }
            dp = ndp;
        }

        for (int i = 0; i < 7; ++i) {
            if (dp[0][i]) return true;
        }
        return false;
    }

Python版本:

class Solution(object):
    def pyramidTransition(self, bottom, allowed):
        from collections import defaultdict
        """
        :type bottom: str
        :type allowed: List[str]
        :rtype: bool
        """
        mem = defaultdict(list)
        for allow in allowed:
            mem[allow[0:2]].append(allow[2])

        dp = [[False] * 10 for i in range(20)]
        n  = len(bottom)
        for i in range(n):
            dp[i][ord(bottom[i]) - ord('A')] = True

        for i in range(n - 1, 0, -1):
            ndp = [[False] * 10 for i in range(20)]
            for j in range(i):
                for l in range(7):
                    for r in range(7):
                        if (dp[j][l] and dp[j + 1][r]):
                            if str(chr(65 + l) + "" + chr(65 + r)) in mem:
                                for c in mem[chr(65 + l) + "" + chr(65 + r)]:
                                    ndp[j][ord(c) - ord('A')] = True
            dp = ndp
        for i in range(7):
            if (dp[0][i]): return True
        return False
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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