From 4b9c5e29ae56953c45d07b3979a9af25e9ac55d6 Mon Sep 17 00:00:00 2001 From: Cy <1433747532@qq.com> Date: Tue, 17 Jun 2025 13:01:20 +0800 Subject: [PATCH] modified by wcy --- .vscode/launch.json | 102 --- .vscode/settings.json | 18 - analyze_database.py | 97 +++ dataset_decoder.py | 2 +- loss.py | 112 +++ model/model.py | 163 ++--- model/model0.py | 603 ++++++++++++++++ model/model1.py | 675 +++++++++++++++++ model/model2.py | 679 ++++++++++++++++++ ...model_ADMIN_Jun-17-112121-2025_Conflict.py | 604 ++++++++++++++++ run_file/DynamicKV-LLM_Mini_Minimind.sh | 8 +- train_pretrain_accelerate.py | 4 +- 12 files changed, 2860 insertions(+), 207 deletions(-) delete mode 100644 .vscode/launch.json delete mode 100644 .vscode/settings.json create mode 100644 analyze_database.py create mode 100644 loss.py create mode 100644 model/model0.py create mode 100644 model/model1.py create mode 100644 model/model2.py create mode 100644 model/model_ADMIN_Jun-17-112121-2025_Conflict.py diff --git a/.vscode/launch.json b/.vscode/launch.json deleted file mode 100644 index 1cb5871..0000000 --- a/.vscode/launch.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "version": "0.2.0", - "configurations": [ - { - "name": "Debug Train Pretrain Accelerate", - "type": "python", - "request": "launch", - "program": "${workspaceFolder}/train_pretrain_accelerate.py", - "console": "integratedTerminal", - "python": "/opt/conda/envs/mini/bin/python", - "cwd": "${workspaceFolder}", - "env": { - "PYTHONPATH": "${workspaceFolder}", - "CUDA_VISIBLE_DEVICES": "0" - }, - "justMyCode": false, - "stopOnEntry": false, - "redirectOutput": true - }, - { - "name": "Debug Train Pretrain Accelerate (Multi-GPU)", - "type": "python", - "request": "launch", - "program": "${workspaceFolder}/train_pretrain_accelerate.py", - "console": "integratedTerminal", - "python": "/opt/conda/envs/mini/bin/python", - "args": [ - "--hidden_size", "512", - "--max_seq_len", "512", - "--n_layers", "8", - "--batch_size", "8", - "--epochs", "1" - ], - "cwd": "${workspaceFolder}", - "env": { - "PYTHONPATH": "${workspaceFolder}", - "CUDA_VISIBLE_DEVICES": "0,1" - }, - "justMyCode": false, - "stopOnEntry": false, - "redirectOutput": true - }, - { - "name": "Debug Train Pretrain Accelerate (Small Test)", - "type": "python", - "request": "launch", - "program": "${workspaceFolder}/train_pretrain_accelerate.py", - "console": "integratedTerminal", - "python": "/opt/conda/envs/mini/bin/python", - "args": [ - "--hidden_size", "512", - "--max_seq_len", "512", - "--n_layers", "8", - "--batch_size", "2", - "--epochs", "1", - "--log_interval", "10", - "--save_interval", "100", - "--accumulation_steps", "4" - ], - "cwd": "${workspaceFolder}", - "env": { - "PYTHONPATH": "${workspaceFolder}", - "CUDA_VISIBLE_DEVICES": "0", - "WANDB_MODE": "offline" - }, - "justMyCode": false, - "stopOnEntry": false, - "redirectOutput": true - }, - { - "name": "Debug ExtractDB Comparison", - "type": "python", - "request": "launch", - "program": "${workspaceFolder}/train_pretrain_accelerate.py", - "console": "integratedTerminal", - "python": "/opt/conda/envs/mini/bin/python", - "args": [ - "--hidden_size", "512", - "--max_seq_len", "256", - "--n_layers", "4", - "--batch_size", "2", - "--epochs", "1", - "--log_interval", "10", - "--save_interval", "200", - "--accumulation_steps", "2", - "--comparison_mode", - "--knowledge_num", "256", - "--knowledge_length", "64", - "--comparison_mode" - ], - "cwd": "${workspaceFolder}", - "env": { - "PYTHONPATH": "${workspaceFolder}", - "CUDA_VISIBLE_DEVICES": "0", - "WANDB_MODE": "offline" - }, - "justMyCode": false, - "stopOnEntry": false, - "redirectOutput": true - } - ] -} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index d5a2ff8..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "python.pythonPath": "/home/iomgaa/miniconda3/envs/accelerate/bin/python", - "python.defaultInterpreterPath": "/home/iomgaa/miniconda3/envs/accelerate/bin/python", - "python.terminal.activateEnvironment": true, - "python.terminal.activateEnvInCurrentTerminal": true, - "python.linting.enabled": true, - "python.linting.pylintEnabled": false, - "python.linting.flake8Enabled": true, - "python.formatting.provider": "black", - "python.analysis.autoImportCompletions": true, - "python.analysis.typeCheckingMode": "off", - "files.exclude": { - "**/__pycache__": true, - "**/*.pyc": true, - "**/.git": false, - "**/wandb": false - } -} \ No newline at end of file diff --git a/analyze_database.py b/analyze_database.py new file mode 100644 index 0000000..c9468b0 --- /dev/null +++ b/analyze_database.py @@ -0,0 +1,97 @@ +import json +import os +import torch +from transformers import AutoTokenizer + +def analyze_database(json_path, tokenizer_path='./model/minimind_tokenizer'): + """分析database_init.json文件中的数据条目数量和质量""" + + print(f"开始分析数据库文件: {json_path}") + + # 1. 加载tokenizer + try: + tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + print(f"成功加载tokenizer: {tokenizer_path}") + except Exception as e: + print(f"加载tokenizer失败: {e}") + return + + # 2. 加载JSON文件 + try: + with open(json_path, 'r', encoding='utf-8') as f: + database_data = json.load(f) + + # 提取sentences列表 + sentences_data = database_data.get('sentences', []) + print(f"加载了 {len(sentences_data)} 条sentences数据") + except Exception as e: + print(f"加载JSON文件失败: {e}") + return + + # 3. 分析句子长度分布 + if len(sentences_data) == 0: + print("没有找到有效的句子数据") + return + + # 按照importance_score排序 + sorted_sentences = sorted(sentences_data, key=lambda x: x.get('importance_score', 0.0), reverse=True) + print(f"按importance_score排序完成，最高分: {sorted_sentences[0].get('importance_score', 0.0)}, 最低分: {sorted_sentences[-1].get('importance_score', 0.0)}") + + # 统计句子长度分布 + token_lengths = [] + pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0 + + # 4. 分析token长度分布 + for i, sentence_data in enumerate(sorted_sentences): + sentence = sentence_data.get('corrected_sentence', '') + if not sentence: + print(f"警告: 第 {i+1} 条数据没有corrected_sentence字段") + continue + + # 将句子转换为tokens + sentence_tokens = tokenizer.encode(sentence, add_special_tokens=False) + token_lengths.append(len(sentence_tokens)) + + if i < 5: # 显示前5条数据样例 + print(f"样例 {i+1}: {sentence[:50]}... (tokens: {len(sentence_tokens)})") + + # 5. 统计分析结果 + token_lengths = torch.tensor(token_lengths) + stats = { + "总条目数": len(sorted_sentences), + "有效条目数": len(token_lengths), + "token长度-平均值": token_lengths.float().mean().item(), + "token长度-最小值": token_lengths.min().item(), + "token长度-最大值": token_lengths.max().item(), + "token长度-中位数": token_lengths.median().item(), + "token长度-标准差": token_lengths.float().std().item(), + } + + # 统计长度分布 + length_bins = { + "小于16": (token_lengths < 16).sum().item(), + "16-32": ((token_lengths >= 16) & (token_lengths < 32)).sum().item(), + "32-64": ((token_lengths >= 32) & (token_lengths < 64)).sum().item(), + "64-128": ((token_lengths >= 64) & (token_lengths < 128)).sum().item(), + "128-256": ((token_lengths >= 128) & (token_lengths < 256)).sum().item(), + "256及以上": (token_lengths >= 256).sum().item(), + } + + # 打印统计信息 + print("\n===== 数据库分析结果 =====") + for key, value in stats.items(): + print(f"{key}: {value}") + + print("\n===== Token长度分布 =====") + for bin_name, count in length_bins.items(): + percentage = (count / len(token_lengths)) * 100 + print(f"{bin_name}: {count} ({percentage:.1f}%)") + + print(f"\n结论: 该数据库文件包含 {stats['有效条目数']} 条有效数据，可以全部填充到知识库中。") + + return stats, length_bins + +if __name__ == "__main__": + # 指定数据库文件路径 + database_path = "./dataset/database_init.json" + analyze_database(database_path) \ No newline at end of file diff --git a/dataset_decoder.py b/dataset_decoder.py index cb5be61..3a95ae3 100644 --- a/dataset_decoder.py +++ b/dataset_decoder.py @@ -132,7 +132,7 @@ def decode_dataset(model_path, output_path, device="cuda"): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Decode MiniMind model's knowledge database") - parser.add_argument("--model_path", type=str, default="out/pretrain_1024.pth", + parser.add_argument("--model_path", type=str, default="out/pretrain_512.pth", help="Path to the model checkpoint") parser.add_argument("--output_path", type=str, default="out/knowledge_db.txt", help="Path to save the decoded text file") diff --git a/loss.py b/loss.py new file mode 100644 index 0000000..4791935 --- /dev/null +++ b/loss.py @@ -0,0 +1,112 @@ +import re +import matplotlib.pyplot as plt +import numpy as np + +def parse_log_file(file_path): + """ + Parse the training log file to extract epoch, step, and loss information. + """ + # Regular expression to match log entries with loss information + pattern = r'\[.*?\] Epoch (\d+)/\d+, Step (\d+)/\d+, Loss: ([\d\.]+)' + + epochs = [] + steps = [] + losses = [] + + try: + with open(file_path, 'r', encoding='utf-8') as f: + log_content = f.read() + + # Find all matches + matches = re.findall(pattern, log_content) + + for match in matches: + epoch = int(match[0]) + step = int(match[1]) + loss = float(match[2]) + + epochs.append(epoch) + steps.append(step) + losses.append(loss) + + return epochs, steps, losses + + except Exception as e: + print(f"Error parsing log file: {e}") + return [], [], [] + +def plot_loss_curve(epochs, steps, losses, output_file='loss_curve.png'): + """ + Plot the loss curve and save it to a file. + """ + plt.figure(figsize=(12, 6)) + + # Create continuous steps for better visualization + continuous_steps = [] + current_max_step = 0 + prev_epoch = 0 + + for i, (e, s) in enumerate(zip(epochs, steps)): + if e > prev_epoch: + # New epoch starts + if i > 0: + current_max_step = continuous_steps[-1] + prev_epoch = e + continuous_steps.append(s + current_max_step) + + # 修改：减小线条宽度和点的大小 + plt.plot(continuous_steps, losses, marker='.', linestyle='-', + color='#1f77b4', markersize=3, linewidth=0.8) + + plt.title('Training Loss Over Steps', fontsize=16) + plt.xlabel('Steps (Continuous)', fontsize=14) + plt.ylabel('Loss', fontsize=14) + plt.grid(True, linestyle='--', alpha=0.5, linewidth=0.5) + + # 修改：减小红线宽度 + for i in range(1, len(epochs)): + if epochs[i] > epochs[i-1]: + plt.axvline(x=continuous_steps[i], color='r', + linestyle='--', alpha=0.5, linewidth=0.8) + + unique_epochs = sorted(set(epochs)) + # Add epoch labels + for e in unique_epochs: + indices = [i for i, epoch in enumerate(epochs) if epoch == e] + if indices: + mid_idx = indices[len(indices) // 2] + plt.text(continuous_steps[mid_idx], max(losses) * 0.95, f'Epoch {e}', + horizontalalignment='center', verticalalignment='center', + fontsize=10, + bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 3}) + + # 移除悬停注释，简化图表 + # for i, (e, s, l) in enumerate(zip(epochs, steps, losses)): + # plt.annotate(...) + + plt.tight_layout() + plt.savefig(output_file, dpi=300) + print(f"Loss curve saved as {output_file}") + + # Also display the data in a table format + print("\nExtracted training data:") + print("-" * 50) + print(f"{'Epoch':<10}{'Step':<10}{'Loss':<15}") + print("-" * 50) + for e, s, l in zip(epochs, steps, losses): + print(f"{e:<10}{s:<10}{l:<15.6f}") + +def main(): + # Specify the path to your log file + log_file_path = 'out/train.log' + + # Parse the log file + epochs, steps, losses = parse_log_file(log_file_path) + + if epochs and steps and losses: + plot_loss_curve(epochs, steps, losses) + else: + print("No data extracted from log file. Please check if the file format is correct.") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/model/model.py b/model/model.py index c94fd2c..572c6c5 100644 --- a/model/model.py +++ b/model/model.py @@ -2,7 +2,7 @@ import math import struct import inspect import time - +#子空间二维分解+梯度更新 from .LMConfig import LMConfig from typing import Any, Optional, Tuple, List, Union import numpy as np @@ -67,23 +67,21 @@ class KnowledgeDataset(nn.Module): ## 数据库参数 self.knowledge_num = params.knowledge_num self.knowledge_length = params.knowledge_length - self.keys = nn.Parameter(torch.randn(self.knowledge_num, self.knowledge_dim) * 0.02, requires_grad=True) - self.product_key_topk = min(16, self.knowledge_num) - # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动 - self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num)) - + # 修改键存储为二维分解空间，设置为可训练参数 + self.num_keys = int(math.sqrt(self.knowledge_num)) + # 确保keys是可训练参数 + self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.key_dim) * 0.02, requires_grad=True) + self.product_key_topk = min(16, self.num_keys) + # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度 self.register_buffer('knowledge_dataset', - torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long) - ) + torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long)) # 计算step数目，用于动态调整权重 self.step_counter = 0 - self.freeze_embedding = False - - + # 移除批次计数器和更新频率相关代码 def intelligent_selection(self, query, all_scores, all_indices): """智能分层选择策略""" @@ -106,7 +104,8 @@ class KnowledgeDataset(nn.Module): candidate_tokens = self.knowledge_dataset[unique_indices] flat_tokens = candidate_tokens.view(-1) flat_embeddings = self.tok_embeddings(flat_tokens) - #获取flat_tokens对应的index + + # 获取flat_tokens对应的index（保留这些变量以便其他地方使用） pre_update_indices = unique_indices.view(-1) pre_update_embeddings = flat_embeddings.view( len(unique_indices), self.knowledge_length, -1 @@ -158,85 +157,87 @@ class KnowledgeDataset(nn.Module): all_best_tokens = torch.stack(batch_best_tokens, dim=0) all_best_tokens_embeddings = torch.stack(batch_best_tokens_embeddings, dim=0) - # 获取 - - # 使用重新计算的embeddings更新self.keys - if self.is_train: - self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings) - - # 更新被修改过的key - with torch.no_grad(): - self.has_update_keys[pre_update_indices] = 1 - return all_best_tokens, all_best_tokens_embeddings - def _update_keys_with_embeddings(self, pre_update_indices, pre_update_embeddings): - if self.freeze_embedding: - return - # 使用pre_update_embeddings更新self.keys + with torch.no_grad(): - pre_update_embeddings = pre_update_embeddings.mean(dim=1) # [337, 512] - pre_update_embeddings = self.to_queries(pre_update_embeddings) - self.keys[pre_update_indices] = pre_update_embeddings + # 1. 计算token序列的平均嵌入 + pre_update_embeddings = pre_update_embeddings.mean(dim=1) # [num_indices, dim] + # 2. 转换维度 + pre_update_embeddings = self.to_queries(pre_update_embeddings) # [num_indices, knowledge_dim] + + # 3. 将one-hot索引转换为子空间索引 + indices_x = pre_update_indices // self.num_keys + indices_y = pre_update_indices % self.num_keys + + # 4. 收集需要更新的唯一子键 + unique_x = torch.unique(indices_x) + unique_y = torch.unique(indices_y) + + # 5. 更新第一个子空间的键 + for k1 in unique_x: + # 找出所有使用该子键的索引 + mask_k1 = (indices_x == k1) + if mask_k1.sum() == 0: + continue + + # 获取所有相关嵌入并计算平均值 + k1_embeddings = pre_update_embeddings[mask_k1] + k1_avg_embedding = k1_embeddings.mean(dim=0) + + # 拆分为两个子空间并更新第一个子空间 + self.keys[k1, 0] = k1_avg_embedding[:self.key_dim] + + # 6. 更新第二个子空间的键 + for k2 in unique_y: + # 找出所有使用该子键的索引 + mask_k2 = (indices_y == k2) + if mask_k2.sum() == 0: + continue + + # 获取所有相关嵌入并计算平均值 + k2_embeddings = pre_update_embeddings[mask_k2] + k2_avg_embedding = k2_embeddings.mean(dim=0) + + # 更新第二个子空间 + self.keys[k2, 1] = k2_avg_embedding[self.key_dim:] - def search_index(self,x): + def search_index(self, x): batch_size, seq_len, dim = x.shape - # collapse sequence dimension by averaging + # 1. 序列维度平均 x_flat = x.mean(dim=1) # [batch_size, dim] - queries = self.to_queries(x_flat) # [batch_size, 2*dim_key] - # queries = queries.reshape(batch_size, 2, self.key_dim) - # queries = queries.permute(1, 0, 2) + # 2. 生成查询向量并重塑为两个子查询 + queries = self.to_queries(x_flat) # [batch_size, knowledge_dim] + queries = queries.reshape(batch_size, 2, self.key_dim) # [batch_size, 2, key_dim] + # 调整维度顺序，使子空间维度位于首位 + queries = queries.permute(1, 0, 2) # [2, batch_size, key_dim] - # 2. 计算queries与keys的相似度 - sim = torch.einsum('b d, k d -> b k', queries, self.keys) + # 3. 计算每个子空间的相似度 + sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys) - # 3. 在两个子空间分别做top-k - scores_and_indices = sim.topk(self.product_key_topk, dim=-1) - scores, indices = scores_and_indices[0], scores_and_indices[1] + # 4. 在两个子空间分别做top-k + scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)] + scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0] + indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1] - # 5. 应用智能分层选择策略 + # 5. 组合两个子空间的结果 + all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2) # [batch_size, topk, topk] + all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2) # [batch_size, topk, topk] + + # 6. 将结果重塑为二维 + all_scores = all_scores.reshape(batch_size, -1) # [batch_size, topk*topk] + all_indices = all_indices.reshape(batch_size, -1) # [batch_size, topk*topk] + + # 7. 选择最终的top-k结果 + scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1) + indices = torch.gather(all_indices, 1, indices_of_indices) + + # 8. 应用智能分层选择策略 best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices) - # 6. 更新1%的keys - if self.is_train: - # 获取未更新过的keys的索引 - not_updated_indices = torch.where(self.has_update_keys == 0)[0] - - # 如果有未更新的keys，随机选择num_update_keys个进行更新 - if len(not_updated_indices) > 0: - num_update_keys = int(self.knowledge_num * 0.01) - perm = torch.randperm(len(not_updated_indices))[:num_update_keys] - perm_num = perm.shape[0] - pre_update_indices = not_updated_indices[perm] - pre_update_tokens = self.knowledge_dataset[pre_update_indices] - pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1)) - pre_update_embeddings = pre_update_embeddings.view(perm_num, self.knowledge_length, -1) - self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings) - # 更新被修改过的key - with torch.no_grad(): - self.has_update_keys[pre_update_indices] = 1 - else: - print("all keys are updated") - # 重置所有keys的更新状态 - self.has_update_keys.zero_() - # 重新获取所有可更新的索引 - not_updated_indices = torch.arange(len(self.has_update_keys), device=self.has_update_keys.device) - num_update_keys = int(self.knowledge_num * 0.01) - perm = torch.randperm(len(not_updated_indices))[:num_update_keys] - pre_update_indices = not_updated_indices[perm] - pre_update_tokens = self.knowledge_dataset[pre_update_indices] - pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1)) - pre_update_embeddings = pre_update_embeddings.view(num_update_keys, self.knowledge_length, -1) - self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings) - # 更新被修改过的key - with torch.no_grad(): - self.has_update_keys[pre_update_indices] = 1 - - - return best_tokens, best_tokens_embeddings class CrossAttention(nn.Module): @@ -522,10 +523,9 @@ class MiniMindLM(PreTrainedModel): start_pos = args.get('start_pos', 0) if self.freeze_embedding and step == 0: self.tok_embeddings.weight.requires_grad = False - # 同时冻结KnowledgeDataset的嵌入更新 - self.knowledge_dataset.freeze_embedding = True + # 移除对knowledge_dataset.freeze_embedding的设置，让键更新由batch_counter控制 + # self.knowledge_dataset.freeze_embedding = True print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad) - print("knowledge_dataset.freeze_embedding: ", self.knowledge_dataset.freeze_embedding) h = self.dropout(self.tok_embeddings(input_ids)) pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)] for l, layer in enumerate(self.layers): @@ -600,4 +600,5 @@ class MiniMindLM(PreTrainedModel): input_ids = torch.cat((input_ids, input_ids_next), dim=1) yield input_ids[:, start:] if input_ids_next.item() == eos_token_id: - break \ No newline at end of file + break + diff --git a/model/model0.py b/model/model0.py new file mode 100644 index 0000000..6911a7d --- /dev/null +++ b/model/model0.py @@ -0,0 +1,603 @@ +import math +import struct +import inspect +import time +#子空间不分解+嵌入更新 +from .LMConfig import LMConfig +from typing import Any, Optional, Tuple, List, Union +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from transformers import PreTrainedModel +from transformers.modeling_outputs import CausalLMOutputWithPast + + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + return self.weight * self._norm(x.float()).type_as(x) + + +def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6): + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + t = torch.arange(end, device=freqs.device) # type: ignore + freqs = torch.outer(t, freqs).float() # type: ignore + pos_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + return pos_cis + + +def apply_rotary_emb(xq, xk, pos_cis): + def unite_shape(pos_cis, x): + ndim = x.ndim + assert 0 <= 1 < ndim + assert pos_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return pos_cis.view(*shape) + + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) + pos_cis = unite_shape(pos_cis, xq_) + xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3) + xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3) + return xq_out.type_as(xq), xk_out.type_as(xk) + +class KnowledgeDataset(nn.Module): + def __init__(self, params, tok_embeddings, is_train=True): + super().__init__() + self.is_train = is_train + self.params = params + self.tok_embeddings = tok_embeddings + + # 嵌入参数 + self.knowledge_dim = params.knowledge_dim + self.key_dim = self.knowledge_dim // 2 + self.to_queries = nn.Sequential( + nn.Linear(params.dim, self.knowledge_dim, bias=False), + ) + + ## 数据库参数 + self.knowledge_num = params.knowledge_num + self.knowledge_length = params.knowledge_length + self.keys = nn.Parameter(torch.randn(self.knowledge_num, self.knowledge_dim) * 0.02, requires_grad=True) + self.product_key_topk = min(16, self.knowledge_num) + + # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动 + self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num)) + + # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度 + self.register_buffer('knowledge_dataset', + torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long) + ) + + # 计算step数目，用于动态调整权重 + self.step_counter = 0 + + self.freeze_embedding = False + + + + def intelligent_selection(self, query, all_scores, all_indices): + """智能分层选择策略""" + if self.is_train == False: + return all_scores, all_indices + + batch_size = all_scores.size(0) + device = all_scores.device + dtype = all_scores.dtype + + # 对每个batch进行分层选择 + enhanced_scores = all_scores.clone() + query_features = query.mean(dim=1) # [batch_size, dim] + + # 预先计算所有候选条目的嵌入（批量优化） + all_candidate_indices = torch.cat([all_indices[i] for i in range(batch_size)], dim=0) + unique_indices, inverse_indices = torch.unique(all_candidate_indices, return_inverse=True) + + # 批量计算唯一候选条目的嵌入 + candidate_tokens = self.knowledge_dataset[unique_indices] + flat_tokens = candidate_tokens.view(-1) + flat_embeddings = self.tok_embeddings(flat_tokens) + #获取flat_tokens对应的index + pre_update_indices = unique_indices.view(-1) + pre_update_embeddings = flat_embeddings.view( + len(unique_indices), self.knowledge_length, -1 + ) + + unique_candidate_features = flat_embeddings.view( + len(unique_indices), self.knowledge_length, -1 + ).mean(dim=1) # [num_unique_candidates, dim] + + # 归一化候选特征（优化相似度计算） + normalized_candidates = F.normalize(unique_candidate_features, dim=-1) + normalized_queries = F.normalize(query_features, dim=-1) + + # 收集所有batch的best_tokens + batch_best_tokens = [] + batch_best_tokens_embeddings = [] + + for batch_idx in range(batch_size): + indices = all_indices[batch_idx] + + # 获取当前batch候选条目对应的特征索引 + start_idx = batch_idx * len(indices) + end_idx = start_idx + len(indices) + batch_inverse_indices = inverse_indices[start_idx:end_idx] + + # 使用预计算的归一化特征进行优化相似度计算 + batch_candidate_features = normalized_candidates[batch_inverse_indices] + query_feature = normalized_queries[batch_idx] + + # 使用矩阵乘法计算余弦相似度 + similarity_scores = torch.mv(batch_candidate_features, query_feature) + + # 找到最大相似度分数的索引 + max_similarity_idx = torch.argmax(similarity_scores) + + # 获取最大相似度对应的候选条目索引 + best_candidate_idx = indices[max_similarity_idx] + + # 获取对应的tokens + best_tokens = self.knowledge_dataset[best_candidate_idx] + best_tokens_embeddings = self.tok_embeddings(best_tokens) + + # 将当前batch的best_tokens添加到列表中 + batch_best_tokens.append(best_tokens) + batch_best_tokens_embeddings.append(best_tokens_embeddings) + + # 将所有batch的best_tokens堆叠成一个张量 + # [batch_size, knowledge_length] + all_best_tokens = torch.stack(batch_best_tokens, dim=0) + all_best_tokens_embeddings = torch.stack(batch_best_tokens_embeddings, dim=0) + + # 获取 + + # 使用重新计算的embeddings更新self.keys + if self.is_train: + self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings) + + # 更新被修改过的key + with torch.no_grad(): + self.has_update_keys[pre_update_indices] = 1 + + return all_best_tokens, all_best_tokens_embeddings + + def _update_keys_with_embeddings(self, pre_update_indices, pre_update_embeddings): + if self.freeze_embedding: + return + # 使用pre_update_embeddings更新self.keys + with torch.no_grad(): + pre_update_embeddings = pre_update_embeddings.mean(dim=1) # [337, 512] + pre_update_embeddings = self.to_queries(pre_update_embeddings) + self.keys[pre_update_indices] = pre_update_embeddings + + def search_index(self,x): + batch_size, seq_len, dim = x.shape + + # collapse sequence dimension by averaging + x_flat = x.mean(dim=1) # [batch_size, dim] + + queries = self.to_queries(x_flat) # [batch_size, 2*dim_key] + # queries = queries.reshape(batch_size, 2, self.key_dim) + # queries = queries.permute(1, 0, 2) + + # 2. 计算queries与keys的相似度 + sim = torch.einsum('b d, k d -> b k', queries, self.keys) + + # 3. 在两个子空间分别做top-k + scores_and_indices = sim.topk(self.product_key_topk, dim=-1) + scores, indices = scores_and_indices[0], scores_and_indices[1] + + # 5. 应用智能分层选择策略 + best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices) + + # 6. 更新1%的keys + if self.is_train: + # 获取未更新过的keys的索引 + not_updated_indices = torch.where(self.has_update_keys == 0)[0] + + # 如果有未更新的keys，随机选择num_update_keys个进行更新 + if len(not_updated_indices) > 0: + num_update_keys = int(self.knowledge_num * 0.01) + perm = torch.randperm(len(not_updated_indices))[:num_update_keys] + perm_num = perm.shape[0] + pre_update_indices = not_updated_indices[perm] + pre_update_tokens = self.knowledge_dataset[pre_update_indices] + pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1)) + pre_update_embeddings = pre_update_embeddings.view(perm_num, self.knowledge_length, -1) + self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings) + # 更新被修改过的key + with torch.no_grad(): + self.has_update_keys[pre_update_indices] = 1 + else: + print("all keys are updated") + # 重置所有keys的更新状态 + self.has_update_keys.zero_() + # 重新获取所有可更新的索引 + not_updated_indices = torch.arange(len(self.has_update_keys), device=self.has_update_keys.device) + num_update_keys = int(self.knowledge_num * 0.01) + perm = torch.randperm(len(not_updated_indices))[:num_update_keys] + pre_update_indices = not_updated_indices[perm] + pre_update_tokens = self.knowledge_dataset[pre_update_indices] + pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1)) + pre_update_embeddings = pre_update_embeddings.view(num_update_keys, self.knowledge_length, -1) + self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings) + # 更新被修改过的key + with torch.no_grad(): + self.has_update_keys[pre_update_indices] = 1 + + + + + return best_tokens, best_tokens_embeddings + +class CrossAttention(nn.Module): + def __init__( + self, + config + ): + super().__init__() + self.config = config + self.num_heads = 8 + self.head_dim = self.config.dim // self.num_heads + self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False) + self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False) + self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False) + + self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False) + + def forward(self, x, db, context_mask=None, pos_emb=None): + batch_size = x.size(0) + + # 分离多头 + q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if pos_emb is not None: + pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + q = q + pos_emb + k = k + pos_emb + v = v + pos_emb + + attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) + + if context_mask is not None: + expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1) + attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10) + + attn_weights = F.softmax(attn_scores, dim=-1) + + context = torch.matmul(attn_weights, v) + + context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim) + + context = self.to_out(context) + + return context + +class Attention(nn.Module): + def __init__(self, args: LMConfig): + super().__init__() + self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads + assert args.n_heads % self.n_kv_heads == 0 + self.n_local_heads = args.n_heads + self.n_local_kv_heads = self.n_kv_heads + self.n_rep = self.n_local_heads // self.n_local_kv_heads + self.head_dim = args.dim // args.n_heads + self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False) + self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) + self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) + self.attn_dropout = nn.Dropout(args.dropout) + self.resid_dropout = nn.Dropout(args.dropout) + self.dropout = args.dropout + self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn + # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") + mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf")) + mask = torch.triu(mask, diagonal=1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, + x: torch.Tensor, + pos_cis: torch.Tensor): + bsz, seq_len, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) + xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + + xq, xk = apply_rotary_emb(xq, xk, pos_cis) + if self.flash and seq_len != 1: + dropout_p = self.dropout if self.training else 0.0 + output = F.scaled_dot_product_attention( + xq, xk, xv, + attn_mask=None, + dropout_p=dropout_p, + is_causal=True + ) + else: + scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim) + scores += self.mask[:, :, :seq_len, :seq_len] + scores = F.softmax(scores.float(), dim=-1).type_as(xq) + scores = self.attn_dropout(scores) + output = scores @ xv + + output = output.transpose(1, 2).reshape(bsz, seq_len, -1) + output = self.resid_dropout(self.wo(output)) + return output + + +class FeedForward(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + if config.hidden_dim is None: + hidden_dim = 4 * config.dim + hidden_dim = int(2 * hidden_dim / 3) + config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of) + self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False) + self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False) + self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False) + self.dropout = nn.Dropout(config.dropout) + + def forward(self, x): + return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) + + +class MoEGate(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + self.config = config + self.top_k = config.num_experts_per_tok + self.n_routed_experts = config.n_routed_experts + + self.scoring_func = config.scoring_func + self.alpha = config.aux_loss_alpha + self.seq_aux = config.seq_aux + + self.norm_topk_prob = config.norm_topk_prob + self.gating_dim = config.dim + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) + self.reset_parameters() + + def reset_parameters(self) -> None: + import torch.nn.init as init + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def forward(self, hidden_states): + bsz, seq_len, h = hidden_states.shape + hidden_states = hidden_states.view(-1, h) + logits = F.linear(hidden_states, self.weight, None) + if self.scoring_func == 'softmax': + scores = logits.softmax(dim=-1) + else: + raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') + + topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) + + if self.top_k > 1 and self.norm_topk_prob: + denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 + topk_weight = topk_weight / denominator + + if self.training and self.alpha > 0.0: + scores_for_aux = scores + aux_topk = self.top_k + topk_idx_for_aux_loss = topk_idx.view(bsz, -1) + if self.seq_aux: + scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) + ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device) + ce.scatter_add_(1, topk_idx_for_aux_loss, + torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_( + seq_len * aux_topk / self.n_routed_experts) + aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha + else: + mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts) + ce = mask_ce.float().mean(0) + Pi = scores_for_aux.mean(0) + fi = ce * self.n_routed_experts + aux_loss = (Pi * fi).sum() * self.alpha + else: + aux_loss = 0 + return topk_idx, topk_weight, aux_loss + + +class MOEFeedForward(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + self.config = config + self.experts = nn.ModuleList([ + FeedForward(config) + for _ in range(config.n_routed_experts) + ]) + self.gate = MoEGate(config) + if config.n_shared_experts is not None: + self.shared_experts = FeedForward(config) + + def forward(self, x): + identity = x + orig_shape = x.shape + bsz, seq_len, _ = x.shape + # 使用门控机制选择专家 + topk_idx, topk_weight, aux_loss = self.gate(x) + x = x.view(-1, x.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + if self.training: + x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0) + y = torch.empty_like(x, dtype=torch.float16) + for i, expert in enumerate(self.experts): + y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致 + y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) + y = y.view(*orig_shape) + else: + y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) + if self.config.n_shared_experts is not None: + y = y + self.shared_experts(identity) + self.aux_loss = aux_loss + return y + + @torch.no_grad() + def moe_infer(self, x, flat_expert_indices, flat_expert_weights): + expert_cache = torch.zeros_like(x) + idxs = flat_expert_indices.argsort() + tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) + token_idxs = idxs // self.config.num_experts_per_tok + # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4） + # 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时 + # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok） + # 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推 + for i, end_idx in enumerate(tokens_per_expert): + start_idx = 0 if i == 0 else tokens_per_expert[i - 1] + if start_idx == end_idx: + continue + expert = self.experts[i] + exp_token_idx = token_idxs[start_idx:end_idx] + expert_tokens = x[exp_token_idx] + expert_out = expert(expert_tokens).to(expert_cache.dtype) + expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) + expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out) + + return expert_cache + + +class MiniMindBlock(nn.Module): + def __init__(self, layer_id: int, config: LMConfig, knowledge_dataset: KnowledgeDataset): + super().__init__() + self.n_heads = config.n_heads + self.dim = config.dim + self.head_dim = config.dim // config.n_heads + self.self_attention = Attention(config) + self.cross_attention = CrossAttention(config) + self.knowledge_dataset = knowledge_dataset + + self.layer_id = layer_id + self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps) + self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps) + self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config) + + def forward(self, x, pos_cis): + h_attn = self.self_attention( + self.attention_norm(x), + pos_cis + ) + db, db_embeddings = self.knowledge_dataset.search_index(h_attn) + h_attn = self.cross_attention(h_attn, db_embeddings) + h = x + h_attn + out = h + self.feed_forward(self.ffn_norm(h)) + return out + + +class MiniMindLM(PreTrainedModel): + config_class = LMConfig + + def __init__(self, params: LMConfig = None): + self.params = params or LMConfig() + super().__init__(self.params) + self.vocab_size, self.n_layers = params.vocab_size, params.n_layers + self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim) + self.dropout = nn.Dropout(params.dropout) + self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings) + self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) for l in range(self.n_layers)]) + self.norm = RMSNorm(params.dim, eps=params.norm_eps) + self.output = nn.Linear(params.dim, params.vocab_size, bias=False) + self.tok_embeddings.weight = self.output.weight + self.register_buffer("pos_cis", + precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta), + persistent=False) + self.OUT = CausalLMOutputWithPast() + self.freeze_embedding = False + + def forward(self, + input_ids: Optional[torch.Tensor] = None, + logits_to_keep: Union[int, torch.Tensor] = 0, + step: int = 0, + **args): + start_pos = args.get('start_pos', 0) + if self.freeze_embedding and step == 0: + self.tok_embeddings.weight.requires_grad = False + # 同时冻结KnowledgeDataset的嵌入更新 + self.knowledge_dataset.freeze_embedding = True + print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad) + print("knowledge_dataset.freeze_embedding: ", self.knowledge_dataset.freeze_embedding) + h = self.dropout(self.tok_embeddings(input_ids)) + pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)] + for l, layer in enumerate(self.layers): + h = layer( + h, pos_cis + ) + + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.output(self.norm(h)[:, slice_indices, :]) + aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward)) + + # 进一步简化，只保留必要的参数 + output = CausalLMOutputWithPast( + logits=logits, + ) + output.hidden_states = h + + output.aux_loss = aux_loss + + return output + + @torch.inference_mode() + def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90, + stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args): + # 流式生成 + if stream: + return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args) + + # 直接生成 + generated = [] + for i in range(input_ids.size(0)): + non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0) + for _ in range(num_return_sequences): + out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args) + tokens_list = [tokens[:, -1:] for tokens in out] + gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad + full_sequence = torch.cat([non_pad, gen], dim=-1) + generated.append(full_sequence) + + max_length = max(seq.size(1) for seq in generated) + generated = [ + torch.cat( + [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)], + dim=-1) + for seq in generated + ] + output = torch.cat(generated, dim=0) + res = output.view(input_ids.size(0) * num_return_sequences, -1) + return res + + def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args): + start, first_seq, past_kvs = input_ids.shape[1], True, None + while input_ids.shape[1] < max_new_tokens - 1: + if first_seq: + out, first_seq = self(input_ids, **args), False + else: + out = self(input_ids[:, -1:], + start_pos=input_ids.shape[1] - 1, **args) + logits, past_kvs = out.logits[:, -1, :], out.past_key_values + logits[:, list(set(input_ids.tolist()[0]))] /= rp + logits /= (temperature + 1e-9) + if top_p is not None and top_p < 1.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) + sorted_probs = F.softmax(sorted_logits, dim=-1) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() + sorted_indices_to_remove[:, 0] = False + indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) + logits[indices_to_remove] = -float('Inf') + input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) + input_ids = torch.cat((input_ids, input_ids_next), dim=1) + yield input_ids[:, start:] + if input_ids_next.item() == eos_token_id: + break \ No newline at end of file diff --git a/model/model1.py b/model/model1.py new file mode 100644 index 0000000..c7b70da --- /dev/null +++ b/model/model1.py @@ -0,0 +1,675 @@ +import math +import struct +import inspect +import time +#子空间二维分解+全局嵌入更新 +from .LMConfig import LMConfig +from typing import Any, Optional, Tuple, List, Union +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from transformers import PreTrainedModel +from transformers.modeling_outputs import CausalLMOutputWithPast + + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + return self.weight * self._norm(x.float()).type_as(x) + + +def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6): + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + t = torch.arange(end, device=freqs.device) # type: ignore + freqs = torch.outer(t, freqs).float() # type: ignore + pos_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + return pos_cis + + +def apply_rotary_emb(xq, xk, pos_cis): + def unite_shape(pos_cis, x): + ndim = x.ndim + assert 0 <= 1 < ndim + assert pos_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return pos_cis.view(*shape) + + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) + pos_cis = unite_shape(pos_cis, xq_) + xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3) + xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3) + return xq_out.type_as(xq), xk_out.type_as(xk) + +class KnowledgeDataset(nn.Module): + def __init__(self, params, tok_embeddings, is_train=True): + super().__init__() + self.is_train = is_train + self.params = params + self.tok_embeddings = tok_embeddings + + # 嵌入参数 + self.knowledge_dim = params.knowledge_dim + self.key_dim = self.knowledge_dim // 2 + self.to_queries = nn.Sequential( + nn.Linear(params.dim, self.knowledge_dim, bias=False), + ) + + ## 数据库参数 + self.knowledge_num = params.knowledge_num + self.knowledge_length = params.knowledge_length + + # 修改键存储为二维分解空间 + self.num_keys = int(math.sqrt(self.knowledge_num)) + self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.key_dim) * 0.02, requires_grad=True) + self.product_key_topk = min(16, self.num_keys) + + # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动 + self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num)) + + # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度 + self.register_buffer('knowledge_dataset', + torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long)) + + # 计算step数目，用于动态调整权重 + self.step_counter = 0 + + self.freeze_embedding = False + + # 添加批次计数器和更新频率 + self.batch_counter = 0 + self.update_frequency = 100 # 每100个批次更新一次 + + def _global_keys_update(self): + """全局更新所有子键""" + # 移除对self.freeze_embedding的检查，确保在调用时总是执行更新 + with torch.no_grad(): + # 创建用于存储每个子键的嵌入和计数的张量 + k1_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device) + k2_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device) + k1_counts = torch.zeros(self.num_keys, device=self.keys.device) + k2_counts = torch.zeros(self.num_keys, device=self.keys.device) + + # 分批处理所有知识条目，避免内存溢出 + batch_size = 1000 # 可根据可用内存调整 + for i in range(0, self.knowledge_num, batch_size): + end_idx = min(i + batch_size, self.knowledge_num) + batch_indices = torch.arange(i, end_idx, device=self.keys.device) + + # 获取批次的嵌入 + batch_tokens = self.knowledge_dataset[batch_indices] + batch_embeddings = self.tok_embeddings(batch_tokens.view(-1)) + batch_embeddings = batch_embeddings.view(len(batch_indices), self.knowledge_length, -1).mean(dim=1) + batch_embeddings = self.to_queries(batch_embeddings) + + # 计算批次中每个条目对应的子键索引 + indices_x = batch_indices // self.num_keys + indices_y = batch_indices % self.num_keys + + # 累加每个子键对应的嵌入 + for j in range(len(batch_indices)): + k1, k2 = indices_x[j].item(), indices_y[j].item() + embedding = batch_embeddings[j] + + # 更新第一个子空间累加值 + k1_embeddings_sum[k1] += embedding[:self.key_dim] + k1_counts[k1] += 1 + + # 更新第二个子空间累加值 + k2_embeddings_sum[k2] += embedding[self.key_dim:] + k2_counts[k2] += 1 + + # 计算平均值并更新键 + # 避免除零错误 + k1_counts = torch.clamp(k1_counts, min=1) + k2_counts = torch.clamp(k2_counts, min=1) + + # 计算每个子键的平均嵌入 + self.keys[:, 0] = k1_embeddings_sum / k1_counts.unsqueeze(1) + self.keys[:, 1] = k2_embeddings_sum / k2_counts.unsqueeze(1) + + print(f"执行了全局键更新，批次: {self.batch_counter}") + + + def intelligent_selection(self, query, all_scores, all_indices): + """智能分层选择策略""" + if self.is_train == False: + return all_scores, all_indices + + batch_size = all_scores.size(0) + device = all_scores.device + dtype = all_scores.dtype + + # 对每个batch进行分层选择 + enhanced_scores = all_scores.clone() + query_features = query.mean(dim=1) # [batch_size, dim] + + # 预先计算所有候选条目的嵌入（批量优化） + all_candidate_indices = torch.cat([all_indices[i] for i in range(batch_size)], dim=0) + unique_indices, inverse_indices = torch.unique(all_candidate_indices, return_inverse=True) + + # 批量计算唯一候选条目的嵌入 + candidate_tokens = self.knowledge_dataset[unique_indices] + flat_tokens = candidate_tokens.view(-1) + flat_embeddings = self.tok_embeddings(flat_tokens) + + # 获取flat_tokens对应的index（保留这些变量以便其他地方使用） + pre_update_indices = unique_indices.view(-1) + pre_update_embeddings = flat_embeddings.view( + len(unique_indices), self.knowledge_length, -1 + ) + + unique_candidate_features = flat_embeddings.view( + len(unique_indices), self.knowledge_length, -1 + ).mean(dim=1) # [num_unique_candidates, dim] + + # 归一化候选特征（优化相似度计算） + normalized_candidates = F.normalize(unique_candidate_features, dim=-1) + normalized_queries = F.normalize(query_features, dim=-1) + + # 收集所有batch的best_tokens + batch_best_tokens = [] + batch_best_tokens_embeddings = [] + + for batch_idx in range(batch_size): + indices = all_indices[batch_idx] + + # 获取当前batch候选条目对应的特征索引 + start_idx = batch_idx * len(indices) + end_idx = start_idx + len(indices) + batch_inverse_indices = inverse_indices[start_idx:end_idx] + + # 使用预计算的归一化特征进行优化相似度计算 + batch_candidate_features = normalized_candidates[batch_inverse_indices] + query_feature = normalized_queries[batch_idx] + + # 使用矩阵乘法计算余弦相似度 + similarity_scores = torch.mv(batch_candidate_features, query_feature) + + # 找到最大相似度分数的索引 + max_similarity_idx = torch.argmax(similarity_scores) + + # 获取最大相似度对应的候选条目索引 + best_candidate_idx = indices[max_similarity_idx] + + # 获取对应的tokens + best_tokens = self.knowledge_dataset[best_candidate_idx] + best_tokens_embeddings = self.tok_embeddings(best_tokens) + + # 将当前batch的best_tokens添加到列表中 + batch_best_tokens.append(best_tokens) + batch_best_tokens_embeddings.append(best_tokens_embeddings) + + # 将所有batch的best_tokens堆叠成一个张量 + # [batch_size, knowledge_length] + all_best_tokens = torch.stack(batch_best_tokens, dim=0) + all_best_tokens_embeddings = torch.stack(batch_best_tokens_embeddings, dim=0) + + with torch.no_grad(): + self.has_update_keys[pre_update_indices] = 1 + + return all_best_tokens, all_best_tokens_embeddings + + + with torch.no_grad(): + # 1. 计算token序列的平均嵌入 + pre_update_embeddings = pre_update_embeddings.mean(dim=1) # [num_indices, dim] + # 2. 转换维度 + pre_update_embeddings = self.to_queries(pre_update_embeddings) # [num_indices, knowledge_dim] + + # 3. 将one-hot索引转换为子空间索引 + indices_x = pre_update_indices // self.num_keys + indices_y = pre_update_indices % self.num_keys + + # 4. 收集需要更新的唯一子键 + unique_x = torch.unique(indices_x) + unique_y = torch.unique(indices_y) + + # 5. 更新第一个子空间的键 + for k1 in unique_x: + # 找出所有使用该子键的索引 + mask_k1 = (indices_x == k1) + if mask_k1.sum() == 0: + continue + + # 获取所有相关嵌入并计算平均值 + k1_embeddings = pre_update_embeddings[mask_k1] + k1_avg_embedding = k1_embeddings.mean(dim=0) + + # 拆分为两个子空间并更新第一个子空间 + self.keys[k1, 0] = k1_avg_embedding[:self.key_dim] + + # 6. 更新第二个子空间的键 + for k2 in unique_y: + # 找出所有使用该子键的索引 + mask_k2 = (indices_y == k2) + if mask_k2.sum() == 0: + continue + + # 获取所有相关嵌入并计算平均值 + k2_embeddings = pre_update_embeddings[mask_k2] + k2_avg_embedding = k2_embeddings.mean(dim=0) + + # 更新第二个子空间 + self.keys[k2, 1] = k2_avg_embedding[self.key_dim:] + + def search_index(self, x): + batch_size, seq_len, dim = x.shape + + # 1. 序列维度平均 + x_flat = x.mean(dim=1) # [batch_size, dim] + + # 2. 生成查询向量并重塑为两个子查询 + queries = self.to_queries(x_flat) # [batch_size, knowledge_dim] + queries = queries.reshape(batch_size, 2, self.key_dim) # [batch_size, 2, key_dim] + # 调整维度顺序，使子空间维度位于首位 + queries = queries.permute(1, 0, 2) # [2, batch_size, key_dim] + + # 3. 计算每个子空间的相似度 + sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys) + + # 4. 在两个子空间分别做top-k + scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)] + scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0] + indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1] + + # 5. 组合两个子空间的结果 + all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2) # [batch_size, topk, topk] + all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2) # [batch_size, topk, topk] + + # 6. 将结果重塑为二维 + all_scores = all_scores.reshape(batch_size, -1) # [batch_size, topk*topk] + all_indices = all_indices.reshape(batch_size, -1) # [batch_size, topk*topk] + + # 7. 选择最终的top-k结果 + scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1) + indices = torch.gather(all_indices, 1, indices_of_indices) + + # 8. 应用智能分层选择策略 + best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices) + + # 9. 更新批次计数并在特定批次执行全局更新 + if self.is_train: + self.batch_counter += 1 + + # 每update_frequency个批次执行一次全局更新，其余时间保持冻结 + if self.batch_counter % self.update_frequency == 0: + # 只在特定批次更新键，无论freeze_embedding状态如何 + self._global_keys_update() + # 标记所有键为已更新状态 + with torch.no_grad(): + self.has_update_keys.fill_(1) + + return best_tokens, best_tokens_embeddings + +class CrossAttention(nn.Module): + def __init__( + self, + config + ): + super().__init__() + self.config = config + self.num_heads = 8 + self.head_dim = self.config.dim // self.num_heads + self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False) + self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False) + self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False) + + self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False) + + def forward(self, x, db, context_mask=None, pos_emb=None): + batch_size = x.size(0) + + # 分离多头 + q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if pos_emb is not None: + pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + q = q + pos_emb + k = k + pos_emb + v = v + pos_emb + + attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) + + if context_mask is not None: + expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1) + attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10) + + attn_weights = F.softmax(attn_scores, dim=-1) + + context = torch.matmul(attn_weights, v) + + context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim) + + context = self.to_out(context) + + return context + +class Attention(nn.Module): + def __init__(self, args: LMConfig): + super().__init__() + self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads + assert args.n_heads % self.n_kv_heads == 0 + self.n_local_heads = args.n_heads + self.n_local_kv_heads = self.n_kv_heads + self.n_rep = self.n_local_heads // self.n_local_kv_heads + self.head_dim = args.dim // args.n_heads + self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False) + self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) + self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) + self.attn_dropout = nn.Dropout(args.dropout) + self.resid_dropout = nn.Dropout(args.dropout) + self.dropout = args.dropout + self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn + # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") + mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf")) + mask = torch.triu(mask, diagonal=1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, + x: torch.Tensor, + pos_cis: torch.Tensor): + bsz, seq_len, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) + xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + + xq, xk = apply_rotary_emb(xq, xk, pos_cis) + if self.flash and seq_len != 1: + dropout_p = self.dropout if self.training else 0.0 + output = F.scaled_dot_product_attention( + xq, xk, xv, + attn_mask=None, + dropout_p=dropout_p, + is_causal=True + ) + else: + scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim) + scores += self.mask[:, :, :seq_len, :seq_len] + scores = F.softmax(scores.float(), dim=-1).type_as(xq) + scores = self.attn_dropout(scores) + output = scores @ xv + + output = output.transpose(1, 2).reshape(bsz, seq_len, -1) + output = self.resid_dropout(self.wo(output)) + return output + + +class FeedForward(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + if config.hidden_dim is None: + hidden_dim = 4 * config.dim + hidden_dim = int(2 * hidden_dim / 3) + config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of) + self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False) + self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False) + self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False) + self.dropout = nn.Dropout(config.dropout) + + def forward(self, x): + return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) + + +class MoEGate(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + self.config = config + self.top_k = config.num_experts_per_tok + self.n_routed_experts = config.n_routed_experts + + self.scoring_func = config.scoring_func + self.alpha = config.aux_loss_alpha + self.seq_aux = config.seq_aux + + self.norm_topk_prob = config.norm_topk_prob + self.gating_dim = config.dim + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) + self.reset_parameters() + + def reset_parameters(self) -> None: + import torch.nn.init as init + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def forward(self, hidden_states): + bsz, seq_len, h = hidden_states.shape + hidden_states = hidden_states.view(-1, h) + logits = F.linear(hidden_states, self.weight, None) + if self.scoring_func == 'softmax': + scores = logits.softmax(dim=-1) + else: + raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') + + topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) + + if self.top_k > 1 and self.norm_topk_prob: + denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 + topk_weight = topk_weight / denominator + + if self.training and self.alpha > 0.0: + scores_for_aux = scores + aux_topk = self.top_k + topk_idx_for_aux_loss = topk_idx.view(bsz, -1) + if self.seq_aux: + scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) + ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device) + ce.scatter_add_(1, topk_idx_for_aux_loss, + torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_( + seq_len * aux_topk / self.n_routed_experts) + aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha + else: + mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts) + ce = mask_ce.float().mean(0) + Pi = scores_for_aux.mean(0) + fi = ce * self.n_routed_experts + aux_loss = (Pi * fi).sum() * self.alpha + else: + aux_loss = 0 + return topk_idx, topk_weight, aux_loss + + +class MOEFeedForward(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + self.config = config + self.experts = nn.ModuleList([ + FeedForward(config) + for _ in range(config.n_routed_experts) + ]) + self.gate = MoEGate(config) + if config.n_shared_experts is not None: + self.shared_experts = FeedForward(config) + + def forward(self, x): + identity = x + orig_shape = x.shape + bsz, seq_len, _ = x.shape + # 使用门控机制选择专家 + topk_idx, topk_weight, aux_loss = self.gate(x) + x = x.view(-1, x.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + if self.training: + x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0) + y = torch.empty_like(x, dtype=torch.float16) + for i, expert in enumerate(self.experts): + y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致 + y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) + y = y.view(*orig_shape) + else: + y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) + if self.config.n_shared_experts is not None: + y = y + self.shared_experts(identity) + self.aux_loss = aux_loss + return y + + @torch.no_grad() + def moe_infer(self, x, flat_expert_indices, flat_expert_weights): + expert_cache = torch.zeros_like(x) + idxs = flat_expert_indices.argsort() + tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) + token_idxs = idxs // self.config.num_experts_per_tok + # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4） + # 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时 + # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok） + # 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推 + for i, end_idx in enumerate(tokens_per_expert): + start_idx = 0 if i == 0 else tokens_per_expert[i - 1] + if start_idx == end_idx: + continue + expert = self.experts[i] + exp_token_idx = token_idxs[start_idx:end_idx] + expert_tokens = x[exp_token_idx] + expert_out = expert(expert_tokens).to(expert_cache.dtype) + expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) + expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out) + + return expert_cache + + +class MiniMindBlock(nn.Module): + def __init__(self, layer_id: int, config: LMConfig, knowledge_dataset: KnowledgeDataset): + super().__init__() + self.n_heads = config.n_heads + self.dim = config.dim + self.head_dim = config.dim // config.n_heads + self.self_attention = Attention(config) + self.cross_attention = CrossAttention(config) + self.knowledge_dataset = knowledge_dataset + + self.layer_id = layer_id + self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps) + self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps) + self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config) + + def forward(self, x, pos_cis): + h_attn = self.self_attention( + self.attention_norm(x), + pos_cis + ) + db, db_embeddings = self.knowledge_dataset.search_index(h_attn) + h_attn = self.cross_attention(h_attn, db_embeddings) + h = x + h_attn + out = h + self.feed_forward(self.ffn_norm(h)) + return out + + +class MiniMindLM(PreTrainedModel): + config_class = LMConfig + + def __init__(self, params: LMConfig = None): + self.params = params or LMConfig() + super().__init__(self.params) + self.vocab_size, self.n_layers = params.vocab_size, params.n_layers + self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim) + self.dropout = nn.Dropout(params.dropout) + self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings) + self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) for l in range(self.n_layers)]) + self.norm = RMSNorm(params.dim, eps=params.norm_eps) + self.output = nn.Linear(params.dim, params.vocab_size, bias=False) + self.tok_embeddings.weight = self.output.weight + self.register_buffer("pos_cis", + precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta), + persistent=False) + self.OUT = CausalLMOutputWithPast() + self.freeze_embedding = False + + def forward(self, + input_ids: Optional[torch.Tensor] = None, + logits_to_keep: Union[int, torch.Tensor] = 0, + step: int = 0, + **args): + start_pos = args.get('start_pos', 0) + if self.freeze_embedding and step == 0: + self.tok_embeddings.weight.requires_grad = False + # 移除对knowledge_dataset.freeze_embedding的设置，让键更新由batch_counter控制 + # self.knowledge_dataset.freeze_embedding = True + print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad) + h = self.dropout(self.tok_embeddings(input_ids)) + pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)] + for l, layer in enumerate(self.layers): + h = layer( + h, pos_cis + ) + + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.output(self.norm(h)[:, slice_indices, :]) + aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward)) + + # 进一步简化，只保留必要的参数 + output = CausalLMOutputWithPast( + logits=logits, + ) + output.hidden_states = h + + output.aux_loss = aux_loss + + return output + + @torch.inference_mode() + def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90, + stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args): + # 流式生成 + if stream: + return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args) + + # 直接生成 + generated = [] + for i in range(input_ids.size(0)): + non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0) + for _ in range(num_return_sequences): + out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args) + tokens_list = [tokens[:, -1:] for tokens in out] + gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad + full_sequence = torch.cat([non_pad, gen], dim=-1) + generated.append(full_sequence) + + max_length = max(seq.size(1) for seq in generated) + generated = [ + torch.cat( + [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)], + dim=-1) + for seq in generated + ] + output = torch.cat(generated, dim=0) + res = output.view(input_ids.size(0) * num_return_sequences, -1) + return res + + def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args): + start, first_seq, past_kvs = input_ids.shape[1], True, None + while input_ids.shape[1] < max_new_tokens - 1: + if first_seq: + out, first_seq = self(input_ids, **args), False + else: + out = self(input_ids[:, -1:], + start_pos=input_ids.shape[1] - 1, **args) + logits, past_kvs = out.logits[:, -1, :], out.past_key_values + logits[:, list(set(input_ids.tolist()[0]))] /= rp + logits /= (temperature + 1e-9) + if top_p is not None and top_p < 1.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) + sorted_probs = F.softmax(sorted_logits, dim=-1) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() + sorted_indices_to_remove[:, 0] = False + indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) + logits[indices_to_remove] = -float('Inf') + input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) + input_ids = torch.cat((input_ids, input_ids_next), dim=1) + yield input_ids[:, start:] + if input_ids_next.item() == eos_token_id: + break + diff --git a/model/model2.py b/model/model2.py new file mode 100644 index 0000000..6798e46 --- /dev/null +++ b/model/model2.py @@ -0,0 +1,679 @@ +import math +import struct +import inspect +import time +#子空间四维分解+全局嵌入更新 +from .LMConfig import LMConfig +from typing import Any, Optional, Tuple, List, Union +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from transformers import PreTrainedModel +from transformers.modeling_outputs import CausalLMOutputWithPast + + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + return self.weight * self._norm(x.float()).type_as(x) + + +def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6): + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + t = torch.arange(end, device=freqs.device) # type: ignore + freqs = torch.outer(t, freqs).float() # type: ignore + pos_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + return pos_cis + + +def apply_rotary_emb(xq, xk, pos_cis): + def unite_shape(pos_cis, x): + ndim = x.ndim + assert 0 <= 1 < ndim + assert pos_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return pos_cis.view(*shape) + + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) + pos_cis = unite_shape(pos_cis, xq_) + xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3) + xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3) + return xq_out.type_as(xq), xk_out.type_as(xk) + +class KnowledgeDataset(nn.Module): + def __init__(self, params, tok_embeddings, is_train=True): + super().__init__() + self.is_train = is_train + self.params = params + self.tok_embeddings = tok_embeddings + + # 嵌入参数 + self.knowledge_dim = params.knowledge_dim + # 修改：子空间维度从原来的一半变为四分之一 + self.key_dim = self.knowledge_dim // 4 + self.to_queries = nn.Sequential( + nn.Linear(params.dim, self.knowledge_dim, bias=False), + ) + + ## 数据库参数 + self.knowledge_num = params.knowledge_num + self.knowledge_length = params.knowledge_length + + # 修改：将键存储从二维分解空间改为四维分解空间 + # 计算每个子空间的键数量(使用四次根号N) + self.num_keys = int(self.knowledge_num ** 0.25) + # 修改子空间个数从2变为4 + self.keys = nn.Parameter(torch.randn(self.num_keys, 4, self.key_dim) * 0.02, requires_grad=True) + self.product_key_topk = min(16, self.num_keys) + + # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动 + self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num)) + + # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度 + self.register_buffer('knowledge_dataset', + torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long)) + + # 计算step数目，用于动态调整权重 + self.step_counter = 0 + + self.freeze_embedding = False + + # 添加批次计数器和更新频率 + self.batch_counter = 0 + self.update_frequency = 100 # 每100个批次更新一次 + + def _global_keys_update(self): + """全局更新所有子键""" + # 移除对self.freeze_embedding的检查，确保在调用时总是执行更新 + with torch.no_grad(): + # 创建用于存储每个子键的嵌入和计数的张量（修改为4个子空间） + k1_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device) + k2_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device) + k3_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device) + k4_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device) + + k1_counts = torch.zeros(self.num_keys, device=self.keys.device) + k2_counts = torch.zeros(self.num_keys, device=self.keys.device) + k3_counts = torch.zeros(self.num_keys, device=self.keys.device) + k4_counts = torch.zeros(self.num_keys, device=self.keys.device) + + # 分批处理所有知识条目，避免内存溢出 + batch_size = 1000 # 可根据可用内存调整 + for i in range(0, self.knowledge_num, batch_size): + end_idx = min(i + batch_size, self.knowledge_num) + batch_indices = torch.arange(i, end_idx, device=self.keys.device) + + # 获取批次的嵌入 + batch_tokens = self.knowledge_dataset[batch_indices] + batch_embeddings = self.tok_embeddings(batch_tokens.view(-1)) + batch_embeddings = batch_embeddings.view(len(batch_indices), self.knowledge_length, -1).mean(dim=1) + batch_embeddings = self.to_queries(batch_embeddings) + + # 计算批次中每个条目对应的子键索引（修改为4个子空间的索引计算） + # 使用整数除法和取模运算来提取四维索引 + temp = batch_indices + indices_4 = temp % self.num_keys + temp = temp // self.num_keys + indices_3 = temp % self.num_keys + temp = temp // self.num_keys + indices_2 = temp % self.num_keys + indices_1 = temp // self.num_keys + + # 累加每个子键对应的嵌入 + for j in range(len(batch_indices)): + k1, k2, k3, k4 = indices_1[j].item(), indices_2[j].item(), indices_3[j].item(), indices_4[j].item() + embedding = batch_embeddings[j] + + # 将嵌入分为四份并分别累加到对应的子空间 + quarter = self.key_dim + k1_embeddings_sum[k1] += embedding[:quarter] + k1_counts[k1] += 1 + + k2_embeddings_sum[k2] += embedding[quarter:2*quarter] + k2_counts[k2] += 1 + + k3_embeddings_sum[k3] += embedding[2*quarter:3*quarter] + k3_counts[k3] += 1 + + k4_embeddings_sum[k4] += embedding[3*quarter:] + k4_counts[k4] += 1 + + # 计算平均值并更新键 + # 避免除零错误 + k1_counts = torch.clamp(k1_counts, min=1) + k2_counts = torch.clamp(k2_counts, min=1) + k3_counts = torch.clamp(k3_counts, min=1) + k4_counts = torch.clamp(k4_counts, min=1) + + # 计算每个子键的平均嵌入 + self.keys[:, 0] = k1_embeddings_sum / k1_counts.unsqueeze(1) + self.keys[:, 1] = k2_embeddings_sum / k2_counts.unsqueeze(1) + self.keys[:, 2] = k3_embeddings_sum / k3_counts.unsqueeze(1) + self.keys[:, 3] = k4_embeddings_sum / k4_counts.unsqueeze(1) + + print(f"执行了全局键更新，批次: {self.batch_counter}") + + + def intelligent_selection(self, query, all_scores, all_indices): + """智能分层选择策略""" + if self.is_train == False: + return all_scores, all_indices + + batch_size = all_scores.size(0) + device = all_scores.device + dtype = all_scores.dtype + + # 对每个batch进行分层选择 + enhanced_scores = all_scores.clone() + query_features = query.mean(dim=1) # [batch_size, dim] + + # 预先计算所有候选条目的嵌入（批量优化） + all_candidate_indices = torch.cat([all_indices[i] for i in range(batch_size)], dim=0) + unique_indices, inverse_indices = torch.unique(all_candidate_indices, return_inverse=True) + + # 批量计算唯一候选条目的嵌入 + candidate_tokens = self.knowledge_dataset[unique_indices] + flat_tokens = candidate_tokens.view(-1) + flat_embeddings = self.tok_embeddings(flat_tokens) + + # 获取flat_tokens对应的index（保留这些变量以便其他地方使用） + pre_update_indices = unique_indices.view(-1) + pre_update_embeddings = flat_embeddings.view( + len(unique_indices), self.knowledge_length, -1 + ) + + unique_candidate_features = flat_embeddings.view( + len(unique_indices), self.knowledge_length, -1 + ).mean(dim=1) # [num_unique_candidates, dim] + + # 归一化候选特征（优化相似度计算） + normalized_candidates = F.normalize(unique_candidate_features, dim=-1) + normalized_queries = F.normalize(query_features, dim=-1) + + # 收集所有batch的best_tokens + batch_best_tokens = [] + batch_best_tokens_embeddings = [] + + for batch_idx in range(batch_size): + indices = all_indices[batch_idx] + + # 获取当前batch候选条目对应的特征索引 + start_idx = batch_idx * len(indices) + end_idx = start_idx + len(indices) + batch_inverse_indices = inverse_indices[start_idx:end_idx] + + # 使用预计算的归一化特征进行优化相似度计算 + batch_candidate_features = normalized_candidates[batch_inverse_indices] + query_feature = normalized_queries[batch_idx] + + # 使用矩阵乘法计算余弦相似度 + similarity_scores = torch.mv(batch_candidate_features, query_feature) + + # 找到最大相似度分数的索引 + max_similarity_idx = torch.argmax(similarity_scores) + + # 获取最大相似度对应的候选条目索引 + best_candidate_idx = indices[max_similarity_idx] + + # 获取对应的tokens + best_tokens = self.knowledge_dataset[best_candidate_idx] + best_tokens_embeddings = self.tok_embeddings(best_tokens) + + # 将当前batch的best_tokens添加到列表中 + batch_best_tokens.append(best_tokens) + batch_best_tokens_embeddings.append(best_tokens_embeddings) + + # 将所有batch的best_tokens堆叠成一个张量 + # [batch_size, knowledge_length] + all_best_tokens = torch.stack(batch_best_tokens, dim=0) + all_best_tokens_embeddings = torch.stack(batch_best_tokens_embeddings, dim=0) + + with torch.no_grad(): + self.has_update_keys[pre_update_indices] = 1 + + return all_best_tokens, all_best_tokens_embeddings + + def search_index(self, x): + batch_size, seq_len, dim = x.shape + + # 1. 序列维度平均 + x_flat = x.mean(dim=1) # [batch_size, dim] + + # 2. 生成查询向量并重塑为四个子查询 + queries = self.to_queries(x_flat) # [batch_size, knowledge_dim] + # 修改：重塑为四个子查询而非两个 + queries = queries.reshape(batch_size, 4, self.key_dim) # [batch_size, 4, key_dim] + # 调整维度顺序，使子空间维度位于首位 + queries = queries.permute(1, 0, 2) # [4, batch_size, key_dim] + + # 3. 计算每个子空间的相似度 + sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys) + + # 4. 在四个子空间分别做top-k + scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(4)] + scores_1, scores_2, scores_3, scores_4 = [scores_and_indices[p][0] for p in range(4)] + indices_1, indices_2, indices_3, indices_4 = [scores_and_indices[p][1] for p in range(4)] + + # 5. 组合四个子空间的结果 + # 首先组合第一、第二子空间 + scores_12 = scores_1.unsqueeze(-1) + scores_2.unsqueeze(-2) # [batch_size, topk, topk] + indices_12_base = (indices_1.unsqueeze(-1) * self.num_keys) + indices_2.unsqueeze(-2) # [batch_size, topk, topk] + + # 然后组合第三、第四子空间 + scores_34 = scores_3.unsqueeze(-1) + scores_4.unsqueeze(-2) # [batch_size, topk, topk] + indices_34_base = (indices_3.unsqueeze(-1) * self.num_keys) + indices_4.unsqueeze(-2) # [batch_size, topk, topk] + + # 最后组合所有子空间 + scores_flat_12 = scores_12.reshape(batch_size, -1) # [batch_size, topk*topk] + indices_flat_12 = indices_12_base.reshape(batch_size, -1) # [batch_size, topk*topk] + + scores_flat_34 = scores_34.reshape(batch_size, -1) # [batch_size, topk*topk] + indices_flat_34 = indices_34_base.reshape(batch_size, -1) # [batch_size, topk*topk] + + # 对12和34组合的结果进行top-k选择 + topk_scores_12, topk_indices_12 = scores_flat_12.topk(min(self.product_key_topk, scores_flat_12.size(1)), dim=-1) + topk_indices_12 = torch.gather(indices_flat_12, 1, topk_indices_12) + + topk_scores_34, topk_indices_34 = scores_flat_34.topk(min(self.product_key_topk, scores_flat_34.size(1)), dim=-1) + topk_indices_34 = torch.gather(indices_flat_34, 1, topk_indices_34) + + # 将12和34的结果组合 + all_scores = topk_scores_12.unsqueeze(-1) + topk_scores_34.unsqueeze(-2) # [batch_size, topk, topk] + all_indices = (topk_indices_12.unsqueeze(-1) * (self.num_keys**2)) + topk_indices_34.unsqueeze(-2) # [batch_size, topk, topk] + + # 6. 将结果重塑为二维 + all_scores = all_scores.reshape(batch_size, -1) # [batch_size, topk*topk] + all_indices = all_indices.reshape(batch_size, -1) # [batch_size, topk*topk] + + # 7. 选择最终的top-k结果 + scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1) + indices = torch.gather(all_indices, 1, indices_of_indices) + + # 8. 应用智能分层选择策略 + best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices) + + # 9. 更新批次计数并在特定批次执行全局更新 + if self.is_train: + self.batch_counter += 1 + + # 每update_frequency个批次执行一次全局更新，其余时间保持冻结 + if self.batch_counter % self.update_frequency == 0: + # 只在特定批次更新键，无论freeze_embedding状态如何 + self._global_keys_update() + # 标记所有键为已更新状态 + with torch.no_grad(): + self.has_update_keys.fill_(1) + + return best_tokens, best_tokens_embeddings + +class CrossAttention(nn.Module): + def __init__( + self, + config + ): + super().__init__() + self.config = config + self.num_heads = 8 + self.head_dim = self.config.dim // self.num_heads + self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False) + self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False) + self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False) + + self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False) + + def forward(self, x, db, context_mask=None, pos_emb=None): + batch_size = x.size(0) + + # 分离多头 + q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if pos_emb is not None: + pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + q = q + pos_emb + k = k + pos_emb + v = v + pos_emb + + attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) + + if context_mask is not None: + expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1) + attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10) + + attn_weights = F.softmax(attn_scores, dim=-1) + + context = torch.matmul(attn_weights, v) + + context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim) + + context = self.to_out(context) + + return context + +class Attention(nn.Module): + def __init__(self, args: LMConfig): + super().__init__() + self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads + assert args.n_heads % self.n_kv_heads == 0 + self.n_local_heads = args.n_heads + self.n_local_kv_heads = self.n_kv_heads + self.n_rep = self.n_local_heads // self.n_local_kv_heads + self.head_dim = args.dim // args.n_heads + self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False) + self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) + self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) + self.attn_dropout = nn.Dropout(args.dropout) + self.resid_dropout = nn.Dropout(args.dropout) + self.dropout = args.dropout + self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn + # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") + mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf")) + mask = torch.triu(mask, diagonal=1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, + x: torch.Tensor, + pos_cis: torch.Tensor): + bsz, seq_len, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) + xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + + xq, xk = apply_rotary_emb(xq, xk, pos_cis) + if self.flash and seq_len != 1: + dropout_p = self.dropout if self.training else 0.0 + output = F.scaled_dot_product_attention( + xq, xk, xv, + attn_mask=None, + dropout_p=dropout_p, + is_causal=True + ) + else: + scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim) + scores += self.mask[:, :, :seq_len, :seq_len] + scores = F.softmax(scores.float(), dim=-1).type_as(xq) + scores = self.attn_dropout(scores) + output = scores @ xv + + output = output.transpose(1, 2).reshape(bsz, seq_len, -1) + output = self.resid_dropout(self.wo(output)) + return output + + +class FeedForward(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + if config.hidden_dim is None: + hidden_dim = 4 * config.dim + hidden_dim = int(2 * hidden_dim / 3) + config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of) + self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False) + self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False) + self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False) + self.dropout = nn.Dropout(config.dropout) + + def forward(self, x): + return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) + + +class MoEGate(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + self.config = config + self.top_k = config.num_experts_per_tok + self.n_routed_experts = config.n_routed_experts + + self.scoring_func = config.scoring_func + self.alpha = config.aux_loss_alpha + self.seq_aux = config.seq_aux + + self.norm_topk_prob = config.norm_topk_prob + self.gating_dim = config.dim + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) + self.reset_parameters() + + def reset_parameters(self) -> None: + import torch.nn.init as init + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def forward(self, hidden_states): + bsz, seq_len, h = hidden_states.shape + hidden_states = hidden_states.view(-1, h) + logits = F.linear(hidden_states, self.weight, None) + if self.scoring_func == 'softmax': + scores = logits.softmax(dim=-1) + else: + raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') + + topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) + + if self.top_k > 1 and self.norm_topk_prob: + denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 + topk_weight = topk_weight / denominator + + if self.training and self.alpha > 0.0: + scores_for_aux = scores + aux_topk = self.top_k + topk_idx_for_aux_loss = topk_idx.view(bsz, -1) + if self.seq_aux: + scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) + ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device) + ce.scatter_add_(1, topk_idx_for_aux_loss, + torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_( + seq_len * aux_topk / self.n_routed_experts) + aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha + else: + mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts) + ce = mask_ce.float().mean(0) + Pi = scores_for_aux.mean(0) + fi = ce * self.n_routed_experts + aux_loss = (Pi * fi).sum() * self.alpha + else: + aux_loss = 0 + return topk_idx, topk_weight, aux_loss + + +class MOEFeedForward(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + self.config = config + self.experts = nn.ModuleList([ + FeedForward(config) + for _ in range(config.n_routed_experts) + ]) + self.gate = MoEGate(config) + if config.n_shared_experts is not None: + self.shared_experts = FeedForward(config) + + def forward(self, x): + identity = x + orig_shape = x.shape + bsz, seq_len, _ = x.shape + # 使用门控机制选择专家 + topk_idx, topk_weight, aux_loss = self.gate(x) + x = x.view(-1, x.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + if self.training: + x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0) + y = torch.empty_like(x, dtype=torch.float16) + for i, expert in enumerate(self.experts): + y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致 + y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) + y = y.view(*orig_shape) + else: + y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) + if self.config.n_shared_experts is not None: + y = y + self.shared_experts(identity) + self.aux_loss = aux_loss + return y + + @torch.no_grad() + def moe_infer(self, x, flat_expert_indices, flat_expert_weights): + expert_cache = torch.zeros_like(x) + idxs = flat_expert_indices.argsort() + tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) + token_idxs = idxs // self.config.num_experts_per_tok + # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4） + # 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时 + # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok） + # 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推 + for i, end_idx in enumerate(tokens_per_expert): + start_idx = 0 if i == 0 else tokens_per_expert[i - 1] + if start_idx == end_idx: + continue + expert = self.experts[i] + exp_token_idx = token_idxs[start_idx:end_idx] + expert_tokens = x[exp_token_idx] + expert_out = expert(expert_tokens).to(expert_cache.dtype) + expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) + expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out) + + return expert_cache + + +class MiniMindBlock(nn.Module): + def __init__(self, layer_id: int, config: LMConfig, knowledge_dataset: KnowledgeDataset): + super().__init__() + self.n_heads = config.n_heads + self.dim = config.dim + self.head_dim = config.dim // config.n_heads + self.self_attention = Attention(config) + self.cross_attention = CrossAttention(config) + self.knowledge_dataset = knowledge_dataset + + self.layer_id = layer_id + self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps) + self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps) + self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config) + + def forward(self, x, pos_cis): + h_attn = self.self_attention( + self.attention_norm(x), + pos_cis + ) + db, db_embeddings = self.knowledge_dataset.search_index(h_attn) + h_attn = self.cross_attention(h_attn, db_embeddings) + h = x + h_attn + out = h + self.feed_forward(self.ffn_norm(h)) + return out + + +class MiniMindLM(PreTrainedModel): + config_class = LMConfig + + def __init__(self, params: LMConfig = None): + self.params = params or LMConfig() + super().__init__(self.params) + self.vocab_size, self.n_layers = params.vocab_size, params.n_layers + self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim) + self.dropout = nn.Dropout(params.dropout) + self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings) + self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) for l in range(self.n_layers)]) + self.norm = RMSNorm(params.dim, eps=params.norm_eps) + self.output = nn.Linear(params.dim, params.vocab_size, bias=False) + self.tok_embeddings.weight = self.output.weight + self.register_buffer("pos_cis", + precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta), + persistent=False) + self.OUT = CausalLMOutputWithPast() + self.freeze_embedding = False + + def forward(self, + input_ids: Optional[torch.Tensor] = None, + logits_to_keep: Union[int, torch.Tensor] = 0, + step: int = 0, + **args): + start_pos = args.get('start_pos', 0) + if self.freeze_embedding and step == 0: + self.tok_embeddings.weight.requires_grad = False + + print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad) + h = self.dropout(self.tok_embeddings(input_ids)) + pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)] + for l, layer in enumerate(self.layers): + h = layer( + h, pos_cis + ) + + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.output(self.norm(h)[:, slice_indices, :]) + aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward)) + + # 进一步简化，只保留必要的参数 + output = CausalLMOutputWithPast( + logits=logits, + ) + output.hidden_states = h + + output.aux_loss = aux_loss + + return output + + @torch.inference_mode() + def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90, + stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args): + # 流式生成 + if stream: + return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args) + + # 直接生成 + generated = [] + for i in range(input_ids.size(0)): + non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0) + for _ in range(num_return_sequences): + out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args) + tokens_list = [tokens[:, -1:] for tokens in out] + gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad + full_sequence = torch.cat([non_pad, gen], dim=-1) + generated.append(full_sequence) + + max_length = max(seq.size(1) for seq in generated) + generated = [ + torch.cat( + [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)], + dim=-1) + for seq in generated + ] + output = torch.cat(generated, dim=0) + res = output.view(input_ids.size(0) * num_return_sequences, -1) + return res + + def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args): + start, first_seq, past_kvs = input_ids.shape[1], True, None + while input_ids.shape[1] < max_new_tokens - 1: + if first_seq: + out, first_seq = self(input_ids, **args), False + else: + out = self(input_ids[:, -1:], + start_pos=input_ids.shape[1] - 1, **args) + logits, past_kvs = out.logits[:, -1, :], out.past_key_values + logits[:, list(set(input_ids.tolist()[0]))] /= rp + logits /= (temperature + 1e-9) + if top_p is not None and top_p < 1.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) + sorted_probs = F.softmax(sorted_logits, dim=-1) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() + sorted_indices_to_remove[:, 0] = False + indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) + logits[indices_to_remove] = -float('Inf') + input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) + input_ids = torch.cat((input_ids, input_ids_next), dim=1) + yield input_ids[:, start:] + if input_ids_next.item() == eos_token_id: + break + diff --git a/model/model_ADMIN_Jun-17-112121-2025_Conflict.py b/model/model_ADMIN_Jun-17-112121-2025_Conflict.py new file mode 100644 index 0000000..572c6c5 --- /dev/null +++ b/model/model_ADMIN_Jun-17-112121-2025_Conflict.py @@ -0,0 +1,604 @@ +import math +import struct +import inspect +import time +#子空间二维分解+梯度更新 +from .LMConfig import LMConfig +from typing import Any, Optional, Tuple, List, Union +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from transformers import PreTrainedModel +from transformers.modeling_outputs import CausalLMOutputWithPast + + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + return self.weight * self._norm(x.float()).type_as(x) + + +def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6): + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + t = torch.arange(end, device=freqs.device) # type: ignore + freqs = torch.outer(t, freqs).float() # type: ignore + pos_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + return pos_cis + + +def apply_rotary_emb(xq, xk, pos_cis): + def unite_shape(pos_cis, x): + ndim = x.ndim + assert 0 <= 1 < ndim + assert pos_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return pos_cis.view(*shape) + + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) + pos_cis = unite_shape(pos_cis, xq_) + xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3) + xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3) + return xq_out.type_as(xq), xk_out.type_as(xk) + +class KnowledgeDataset(nn.Module): + def __init__(self, params, tok_embeddings, is_train=True): + super().__init__() + self.is_train = is_train + self.params = params + self.tok_embeddings = tok_embeddings + + # 嵌入参数 + self.knowledge_dim = params.knowledge_dim + self.key_dim = self.knowledge_dim // 2 + self.to_queries = nn.Sequential( + nn.Linear(params.dim, self.knowledge_dim, bias=False), + ) + + ## 数据库参数 + self.knowledge_num = params.knowledge_num + self.knowledge_length = params.knowledge_length + + # 修改键存储为二维分解空间，设置为可训练参数 + self.num_keys = int(math.sqrt(self.knowledge_num)) + # 确保keys是可训练参数 + self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.key_dim) * 0.02, requires_grad=True) + self.product_key_topk = min(16, self.num_keys) + + # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度 + self.register_buffer('knowledge_dataset', + torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long)) + + # 计算step数目，用于动态调整权重 + self.step_counter = 0 + + # 移除批次计数器和更新频率相关代码 + + def intelligent_selection(self, query, all_scores, all_indices): + """智能分层选择策略""" + if self.is_train == False: + return all_scores, all_indices + + batch_size = all_scores.size(0) + device = all_scores.device + dtype = all_scores.dtype + + # 对每个batch进行分层选择 + enhanced_scores = all_scores.clone() + query_features = query.mean(dim=1) # [batch_size, dim] + + # 预先计算所有候选条目的嵌入（批量优化） + all_candidate_indices = torch.cat([all_indices[i] for i in range(batch_size)], dim=0) + unique_indices, inverse_indices = torch.unique(all_candidate_indices, return_inverse=True) + + # 批量计算唯一候选条目的嵌入 + candidate_tokens = self.knowledge_dataset[unique_indices] + flat_tokens = candidate_tokens.view(-1) + flat_embeddings = self.tok_embeddings(flat_tokens) + + # 获取flat_tokens对应的index（保留这些变量以便其他地方使用） + pre_update_indices = unique_indices.view(-1) + pre_update_embeddings = flat_embeddings.view( + len(unique_indices), self.knowledge_length, -1 + ) + + unique_candidate_features = flat_embeddings.view( + len(unique_indices), self.knowledge_length, -1 + ).mean(dim=1) # [num_unique_candidates, dim] + + # 归一化候选特征（优化相似度计算） + normalized_candidates = F.normalize(unique_candidate_features, dim=-1) + normalized_queries = F.normalize(query_features, dim=-1) + + # 收集所有batch的best_tokens + batch_best_tokens = [] + batch_best_tokens_embeddings = [] + + for batch_idx in range(batch_size): + indices = all_indices[batch_idx] + + # 获取当前batch候选条目对应的特征索引 + start_idx = batch_idx * len(indices) + end_idx = start_idx + len(indices) + batch_inverse_indices = inverse_indices[start_idx:end_idx] + + # 使用预计算的归一化特征进行优化相似度计算 + batch_candidate_features = normalized_candidates[batch_inverse_indices] + query_feature = normalized_queries[batch_idx] + + # 使用矩阵乘法计算余弦相似度 + similarity_scores = torch.mv(batch_candidate_features, query_feature) + + # 找到最大相似度分数的索引 + max_similarity_idx = torch.argmax(similarity_scores) + + # 获取最大相似度对应的候选条目索引 + best_candidate_idx = indices[max_similarity_idx] + + # 获取对应的tokens + best_tokens = self.knowledge_dataset[best_candidate_idx] + best_tokens_embeddings = self.tok_embeddings(best_tokens) + + # 将当前batch的best_tokens添加到列表中 + batch_best_tokens.append(best_tokens) + batch_best_tokens_embeddings.append(best_tokens_embeddings) + + # 将所有batch的best_tokens堆叠成一个张量 + # [batch_size, knowledge_length] + all_best_tokens = torch.stack(batch_best_tokens, dim=0) + all_best_tokens_embeddings = torch.stack(batch_best_tokens_embeddings, dim=0) + + return all_best_tokens, all_best_tokens_embeddings + + + with torch.no_grad(): + # 1. 计算token序列的平均嵌入 + pre_update_embeddings = pre_update_embeddings.mean(dim=1) # [num_indices, dim] + # 2. 转换维度 + pre_update_embeddings = self.to_queries(pre_update_embeddings) # [num_indices, knowledge_dim] + + # 3. 将one-hot索引转换为子空间索引 + indices_x = pre_update_indices // self.num_keys + indices_y = pre_update_indices % self.num_keys + + # 4. 收集需要更新的唯一子键 + unique_x = torch.unique(indices_x) + unique_y = torch.unique(indices_y) + + # 5. 更新第一个子空间的键 + for k1 in unique_x: + # 找出所有使用该子键的索引 + mask_k1 = (indices_x == k1) + if mask_k1.sum() == 0: + continue + + # 获取所有相关嵌入并计算平均值 + k1_embeddings = pre_update_embeddings[mask_k1] + k1_avg_embedding = k1_embeddings.mean(dim=0) + + # 拆分为两个子空间并更新第一个子空间 + self.keys[k1, 0] = k1_avg_embedding[:self.key_dim] + + # 6. 更新第二个子空间的键 + for k2 in unique_y: + # 找出所有使用该子键的索引 + mask_k2 = (indices_y == k2) + if mask_k2.sum() == 0: + continue + + # 获取所有相关嵌入并计算平均值 + k2_embeddings = pre_update_embeddings[mask_k2] + k2_avg_embedding = k2_embeddings.mean(dim=0) + + # 更新第二个子空间 + self.keys[k2, 1] = k2_avg_embedding[self.key_dim:] + + def search_index(self, x): + batch_size, seq_len, dim = x.shape + + # 1. 序列维度平均 + x_flat = x.mean(dim=1) # [batch_size, dim] + + # 2. 生成查询向量并重塑为两个子查询 + queries = self.to_queries(x_flat) # [batch_size, knowledge_dim] + queries = queries.reshape(batch_size, 2, self.key_dim) # [batch_size, 2, key_dim] + # 调整维度顺序，使子空间维度位于首位 + queries = queries.permute(1, 0, 2) # [2, batch_size, key_dim] + + # 3. 计算每个子空间的相似度 + sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys) + + # 4. 在两个子空间分别做top-k + scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)] + scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0] + indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1] + + # 5. 组合两个子空间的结果 + all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2) # [batch_size, topk, topk] + all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2) # [batch_size, topk, topk] + + # 6. 将结果重塑为二维 + all_scores = all_scores.reshape(batch_size, -1) # [batch_size, topk*topk] + all_indices = all_indices.reshape(batch_size, -1) # [batch_size, topk*topk] + + # 7. 选择最终的top-k结果 + scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1) + indices = torch.gather(all_indices, 1, indices_of_indices) + + # 8. 应用智能分层选择策略 + best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices) + + + return best_tokens, best_tokens_embeddings + +class CrossAttention(nn.Module): + def __init__( + self, + config + ): + super().__init__() + self.config = config + self.num_heads = 8 + self.head_dim = self.config.dim // self.num_heads + self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False) + self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False) + self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False) + + self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False) + + def forward(self, x, db, context_mask=None, pos_emb=None): + batch_size = x.size(0) + + # 分离多头 + q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if pos_emb is not None: + pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + q = q + pos_emb + k = k + pos_emb + v = v + pos_emb + + attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) + + if context_mask is not None: + expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1) + attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10) + + attn_weights = F.softmax(attn_scores, dim=-1) + + context = torch.matmul(attn_weights, v) + + context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim) + + context = self.to_out(context) + + return context + +class Attention(nn.Module): + def __init__(self, args: LMConfig): + super().__init__() + self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads + assert args.n_heads % self.n_kv_heads == 0 + self.n_local_heads = args.n_heads + self.n_local_kv_heads = self.n_kv_heads + self.n_rep = self.n_local_heads // self.n_local_kv_heads + self.head_dim = args.dim // args.n_heads + self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False) + self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) + self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) + self.attn_dropout = nn.Dropout(args.dropout) + self.resid_dropout = nn.Dropout(args.dropout) + self.dropout = args.dropout + self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn + # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") + mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf")) + mask = torch.triu(mask, diagonal=1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, + x: torch.Tensor, + pos_cis: torch.Tensor): + bsz, seq_len, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) + xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + + xq, xk = apply_rotary_emb(xq, xk, pos_cis) + if self.flash and seq_len != 1: + dropout_p = self.dropout if self.training else 0.0 + output = F.scaled_dot_product_attention( + xq, xk, xv, + attn_mask=None, + dropout_p=dropout_p, + is_causal=True + ) + else: + scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim) + scores += self.mask[:, :, :seq_len, :seq_len] + scores = F.softmax(scores.float(), dim=-1).type_as(xq) + scores = self.attn_dropout(scores) + output = scores @ xv + + output = output.transpose(1, 2).reshape(bsz, seq_len, -1) + output = self.resid_dropout(self.wo(output)) + return output + + +class FeedForward(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + if config.hidden_dim is None: + hidden_dim = 4 * config.dim + hidden_dim = int(2 * hidden_dim / 3) + config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of) + self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False) + self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False) + self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False) + self.dropout = nn.Dropout(config.dropout) + + def forward(self, x): + return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) + + +class MoEGate(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + self.config = config + self.top_k = config.num_experts_per_tok + self.n_routed_experts = config.n_routed_experts + + self.scoring_func = config.scoring_func + self.alpha = config.aux_loss_alpha + self.seq_aux = config.seq_aux + + self.norm_topk_prob = config.norm_topk_prob + self.gating_dim = config.dim + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) + self.reset_parameters() + + def reset_parameters(self) -> None: + import torch.nn.init as init + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def forward(self, hidden_states): + bsz, seq_len, h = hidden_states.shape + hidden_states = hidden_states.view(-1, h) + logits = F.linear(hidden_states, self.weight, None) + if self.scoring_func == 'softmax': + scores = logits.softmax(dim=-1) + else: + raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') + + topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) + + if self.top_k > 1 and self.norm_topk_prob: + denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 + topk_weight = topk_weight / denominator + + if self.training and self.alpha > 0.0: + scores_for_aux = scores + aux_topk = self.top_k + topk_idx_for_aux_loss = topk_idx.view(bsz, -1) + if self.seq_aux: + scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) + ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device) + ce.scatter_add_(1, topk_idx_for_aux_loss, + torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_( + seq_len * aux_topk / self.n_routed_experts) + aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha + else: + mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts) + ce = mask_ce.float().mean(0) + Pi = scores_for_aux.mean(0) + fi = ce * self.n_routed_experts + aux_loss = (Pi * fi).sum() * self.alpha + else: + aux_loss = 0 + return topk_idx, topk_weight, aux_loss + + +class MOEFeedForward(nn.Module): + def __init__(self, config: LMConfig): + super().__init__() + self.config = config + self.experts = nn.ModuleList([ + FeedForward(config) + for _ in range(config.n_routed_experts) + ]) + self.gate = MoEGate(config) + if config.n_shared_experts is not None: + self.shared_experts = FeedForward(config) + + def forward(self, x): + identity = x + orig_shape = x.shape + bsz, seq_len, _ = x.shape + # 使用门控机制选择专家 + topk_idx, topk_weight, aux_loss = self.gate(x) + x = x.view(-1, x.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + if self.training: + x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0) + y = torch.empty_like(x, dtype=torch.float16) + for i, expert in enumerate(self.experts): + y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致 + y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) + y = y.view(*orig_shape) + else: + y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) + if self.config.n_shared_experts is not None: + y = y + self.shared_experts(identity) + self.aux_loss = aux_loss + return y + + @torch.no_grad() + def moe_infer(self, x, flat_expert_indices, flat_expert_weights): + expert_cache = torch.zeros_like(x) + idxs = flat_expert_indices.argsort() + tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) + token_idxs = idxs // self.config.num_experts_per_tok + # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4） + # 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时 + # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok） + # 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推 + for i, end_idx in enumerate(tokens_per_expert): + start_idx = 0 if i == 0 else tokens_per_expert[i - 1] + if start_idx == end_idx: + continue + expert = self.experts[i] + exp_token_idx = token_idxs[start_idx:end_idx] + expert_tokens = x[exp_token_idx] + expert_out = expert(expert_tokens).to(expert_cache.dtype) + expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) + expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out) + + return expert_cache + + +class MiniMindBlock(nn.Module): + def __init__(self, layer_id: int, config: LMConfig, knowledge_dataset: KnowledgeDataset): + super().__init__() + self.n_heads = config.n_heads + self.dim = config.dim + self.head_dim = config.dim // config.n_heads + self.self_attention = Attention(config) + self.cross_attention = CrossAttention(config) + self.knowledge_dataset = knowledge_dataset + + self.layer_id = layer_id + self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps) + self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps) + self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config) + + def forward(self, x, pos_cis): + h_attn = self.self_attention( + self.attention_norm(x), + pos_cis + ) + db, db_embeddings = self.knowledge_dataset.search_index(h_attn) + h_attn = self.cross_attention(h_attn, db_embeddings) + h = x + h_attn + out = h + self.feed_forward(self.ffn_norm(h)) + return out + + +class MiniMindLM(PreTrainedModel): + config_class = LMConfig + + def __init__(self, params: LMConfig = None): + self.params = params or LMConfig() + super().__init__(self.params) + self.vocab_size, self.n_layers = params.vocab_size, params.n_layers + self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim) + self.dropout = nn.Dropout(params.dropout) + self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings) + self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) for l in range(self.n_layers)]) + self.norm = RMSNorm(params.dim, eps=params.norm_eps) + self.output = nn.Linear(params.dim, params.vocab_size, bias=False) + self.tok_embeddings.weight = self.output.weight + self.register_buffer("pos_cis", + precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta), + persistent=False) + self.OUT = CausalLMOutputWithPast() + self.freeze_embedding = False + + def forward(self, + input_ids: Optional[torch.Tensor] = None, + logits_to_keep: Union[int, torch.Tensor] = 0, + step: int = 0, + **args): + start_pos = args.get('start_pos', 0) + if self.freeze_embedding and step == 0: + self.tok_embeddings.weight.requires_grad = False + # 移除对knowledge_dataset.freeze_embedding的设置，让键更新由batch_counter控制 + # self.knowledge_dataset.freeze_embedding = True + print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad) + h = self.dropout(self.tok_embeddings(input_ids)) + pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)] + for l, layer in enumerate(self.layers): + h = layer( + h, pos_cis + ) + + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.output(self.norm(h)[:, slice_indices, :]) + aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward)) + + # 进一步简化，只保留必要的参数 + output = CausalLMOutputWithPast( + logits=logits, + ) + output.hidden_states = h + + output.aux_loss = aux_loss + + return output + + @torch.inference_mode() + def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90, + stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args): + # 流式生成 + if stream: + return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args) + + # 直接生成 + generated = [] + for i in range(input_ids.size(0)): + non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0) + for _ in range(num_return_sequences): + out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args) + tokens_list = [tokens[:, -1:] for tokens in out] + gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad + full_sequence = torch.cat([non_pad, gen], dim=-1) + generated.append(full_sequence) + + max_length = max(seq.size(1) for seq in generated) + generated = [ + torch.cat( + [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)], + dim=-1) + for seq in generated + ] + output = torch.cat(generated, dim=0) + res = output.view(input_ids.size(0) * num_return_sequences, -1) + return res + + def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args): + start, first_seq, past_kvs = input_ids.shape[1], True, None + while input_ids.shape[1] < max_new_tokens - 1: + if first_seq: + out, first_seq = self(input_ids, **args), False + else: + out = self(input_ids[:, -1:], + start_pos=input_ids.shape[1] - 1, **args) + logits, past_kvs = out.logits[:, -1, :], out.past_key_values + logits[:, list(set(input_ids.tolist()[0]))] /= rp + logits /= (temperature + 1e-9) + if top_p is not None and top_p < 1.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) + sorted_probs = F.softmax(sorted_logits, dim=-1) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() + sorted_indices_to_remove[:, 0] = False + indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) + logits[indices_to_remove] = -float('Inf') + input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) + input_ids = torch.cat((input_ids, input_ids_next), dim=1) + yield input_ids[:, start:] + if input_ids_next.item() == eos_token_id: + break + diff --git a/run_file/DynamicKV-LLM_Mini_Minimind.sh b/run_file/DynamicKV-LLM_Mini_Minimind.sh index 45fe379..93b6a9b 100644 --- a/run_file/DynamicKV-LLM_Mini_Minimind.sh +++ b/run_file/DynamicKV-LLM_Mini_Minimind.sh @@ -1,8 +1,10 @@ #!/bin/bash # 激活conda环境 -source $(conda info --base)/etc/profile.d/conda.sh -conda activate mini +#source $(conda info --base)/etc/profile.d/conda.sh +#conda activate mini +source /mnt/wcy/miniconda/bin/activate +conda activate accelerate # 设置环境变量以帮助调试 export NCCL_DEBUG=INFO @@ -26,7 +28,7 @@ export PYTHONFAULTHANDLER=1 # --profile_interval 10 # 方法2: 使用命令行参数直接配置accelerate -CUDA_VISIBLE_DEVICES=0 /opt/conda/envs/mini/bin/python -m accelerate.commands.launch \ +CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch \ --num_processes=1 \ --mixed_precision=bf16 \ --main_process_port=29500 \ diff --git a/train_pretrain_accelerate.py b/train_pretrain_accelerate.py index e00e6a4..aae2e81 100644 --- a/train_pretrain_accelerate.py +++ b/train_pretrain_accelerate.py @@ -461,14 +461,14 @@ def main(): parser.add_argument("--profile", action="store_true", default=True, help="启用性能分析") parser.add_argument("--profile_interval", type=int, default=10, help="性能分析打印间隔（步数）") parser.add_argument("--use_flash_attn", action="store_true", default=True, help="启用FlashAttention") - parser.add_argument("--knowledge_num", type=int, default=8192,help="知识库的数据数目") + parser.add_argument("--knowledge_num", type=int, default=960400,help="知识库的数据数目") parser.add_argument("--knowledge_length", type=int, default=32,help="知识库的句子长度") parser.add_argument("--database_init_path", type=str, default="./dataset/database_init.json", help="数据库初始化路径") parser.add_argument("--fast_clustering", action="store_true", default=True, help="使用快速近似聚类算法（适用于大数据集）") parser.add_argument("--cluster_cache_path", type=str, default="./cache/cluster_tokens_single.pt", help="聚类结果缓存文件路径") parser.add_argument("--recompute_clusters", action="store_true", default=False, help="强制重新计算聚类，忽略缓存文件") args = parser.parse_args() - + ######################################################### # 初始化accelerator和deepspeed #########################################################