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def __init__(self, config): super(Model, self).__init__() if config.embedding_pretrained is not None: self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False) else: self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1) self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers, bidirectional=True, batch_first=True, dropout=config.dropout) self.fc = nn.Linear(config.hidden_size * 2, config.num_classes)

时间: 2024-04-28 18:20:19 浏览: 237
这是一个神经网络模型的初始化函数，它包含以下几个部分： 1. 调用父类的初始化函数，即nn.Module的__init__()方法。 2. 根据config中的embedding_pretrained是否为None，选择使用预训练的词向量还是随机初始化的词向量。如果使用预训练的词向量，则调用nn.Embedding.from_pretrained()方法加载预训练的词向量，否则使用nn.Embedding()方法随机初始化词向量。 3. 初始化一个LSTM层，其中输入维度为config.embed，隐藏状态维度为config.hidden_size，层数为config.num_layers，双向LSTM，batch_first设置为True，dropout为config.dropout。 4. 初始化一个全连接层，将LSTM输出的双向隐藏状态拼接后，经过一个线性变换得到最终的输出结果，输出维度为config.num_classes。 这个模型的输入是一个batch的词索引序列，输出是每个样本对应的类别。
相关问题

class Model(nn.Module): def __init__(self, config): super(Model, self).__init__() if config.embedding_pretrained is not None: self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False) else: self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1) self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers, bidirectional=True, batch_first=True, dropout=config.dropout) self.fc = nn.Linear(config.hidden_size * 2, config.num_classes) def forward(self, x): x, _ = x out = self.embedding(x) # [batch_size, seq_len, embeding]=[128, 32, 300] out, _ = self.lstm(out) out = self.fc(out[:, -1, :]) # 句子最后时刻的 hidden state return out

这段代码是一个基于 LSTM 的文本分类模型的实现，模型的输入是一个由文本数据构成的批次（batch），输出是这个批次中每个文本数据的分类结果。在该模型中，首先根据配置信息创建了一个 Embedding 层，用于将每个词汇映射为一个向量。如果配置信息中指定了预训练的 Embedding 权重，则使用预训练的权重；否则，随机初始化 Embedding 权重。接下来，将 Embedding 层的输出输入到一个多层 LSTM 网络中进行处理，得到每个文本数据的最后一个时刻上的 hidden state。最后，将这些 hidden state 输入到一个全连接层中进行分类，得到分类结果。在 forward 函数中，输入数据 x 是一个元组，其中第一个元素是输入数据，第二个元素是序列长度，但是在本模型中并没有用到序列长度，只用到了输入数据。在 forward 函数中，首先将输入数据输入到 Embedding 层中，得到词向量表达；然后将词向量表达输入到 LSTM 网络中进行处理，得到每个文本数据的 hidden state；最后，将 hidden state 输入到全连接层中进行分类，得到分类结果。

class Model(nn.Module): def init(self, config): super(Model, self).init() if config.embedding_pretrained is not None: self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False) else: self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1) self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers, bidirectional=True, batch_first=True, dropout=config.dropout) self.fc = nn.Linear(config.hidden_size * 2, config.num_classes) def forward(self, x): x, _ = x out = self.embedding(x) # [batch_size, seq_len, embeding]=[128, 32, 300] out, _ = self.lstm(out) out = self.fc(out[:, -1, :]) # 句子最后时刻的 hidden state return out

这段代码是一个PyTorch的模型定义，其中包括embedding层、LSTM层和全连接层。模型的输入是x，输出是out。其中，输入x的形状为[batch_size, seq_len]，表示一批输入数据中每个序列的长度为seq_len，总共有batch_size个序列。模型会将输入的每个词转换为向量形式，然后通过LSTM层对整个序列进行编码，最后使用全连接层将编码结果映射到目标类别的概率分布上。
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# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """wide and deep model""" import time import numpy as np from mindspore import nn, context from mindspore import Parameter, ParameterTuple import mindspore.common.dtype as mstype import mindspore.ops as ops from mindspore.nn import Dropout from mindspore.nn.optim import Adam, FTRL from mindspore.common.initializer import Uniform, initializer from mindspore.context import ParallelMode from mindspore.nn.wrap.grad_reducer import DistributedGradReducer from mindspore.communication.management import get_group_size np_type = np.float32 ms_type = mstype.float32 def init_method(method, shape, name, max_val=1.0): ''' parameter init method ''' if method in ['uniform']: params = Parameter(initializer( Uniform(max_val), shape, ms_type), name=name) elif method == "one": params = Parameter(initializer("ones", shape, ms_type), name=name) elif method == 'zero': params = Parameter(initializer("zeros", shape, ms_type), name=name) elif method == "normal": params = Parameter(initializer("normal", shape, ms_type), name=name) return params def init_var_dict(init_args, in_vars): ''' var init function ''' var_map = {} _, _max_val = init_args for _, item in enumerate(in_vars): key, shape, method = item if key not in var_map.keys(): if method in ['random', 'uniform']: var_map[key] = Parameter(initializer( Uniform(_max_val), shape, ms_type), name=key) elif method == "one": var_map[key] = Parameter(initializer( "ones", shape, ms_type), name=key) elif method == "zero": var_map[key] = Parameter(initializer( "zeros", shape, ms_type), name=key) elif method == 'normal': var_map[key] = Parameter(initializer( "normal", shape, ms_type), name=key) return var_map class DenseLayer(nn.Cell): """ Dense Layer for Deep Layer of WideDeep Model; Containing: activation, matmul, bias_add; Args: """ def __init__(self, input_dim, output_dim, weight_bias_init, act_str, keep_prob=0.5, use_activation=True, convert_dtype=True, drop_out=False): super(DenseLayer, self).__init__() weight_init, bias_init = weight_bias_init self.weight = init_method( weight_init, [input_dim, output_dim], name="weight") self.bias = init_method(bias_init, [output_dim], name="bias") self.act_func = self._init_activation(act_str) self.matmul = ops.MatMul(transpose_b=False) self.bias_add = ops.BiasAdd() self.cast = ops.Cast() self.dropout = Dropout(keep_prob=(1 - keep_prob)) self.use_activation = use_activation self.convert_dtype = convert_dtype self.drop_out = drop_out def _init_activation(self, act_str): act_str = act_str.lower() if act_str == "relu": act_func = ops.ReLU() elif act_str == "sigmoid": act_func = ops.Sigmoid() elif act_str == "tanh": act_func = ops.Tanh() return act_func def construct(self, x): ''' Construct Dense layer ''' if self.training and self.drop_out: x = self.dropout(x) if self.convert_dtype: x = self.cast(x, mstype.float16) weight = self.cast(self.weight, mstype.float16) bias = self.cast(self.bias, mstype.float16) wx = self.matmul(x, weight) wx = self.bias_add(wx, bias) if self.use_activation: wx = self.act_func(wx) wx = self.cast(wx, mstype.float32) else: wx = self.matmul(x, self.weight) wx = self.bias_add(wx, self.bias) if self.use_activation: wx = self.act_func(wx) return wx class WideDeepModel(nn.Cell): """ From paper: " Wide & Deep Learning for Recommender Systems" Args: config (Class): The default config of Wide&Deep """ def __init__(self, config): super(WideDeepModel, self).__init__() self.batch_size = config.batch_size host_device_mix = bool(config.host_device_mix) parameter_server = bool(config.parameter_server) parallel_mode = context.get_auto_parallel_context("parallel_mode") is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) if is_auto_parallel: self.batch_size = self.batch_size * get_group_size() sparse = config.sparse self.field_size = config.field_size self.emb_dim = config.emb_dim self.weight_init, self.bias_init = config.weight_bias_init self.deep_input_dims = self.field_size * self.emb_dim self.all_dim_list = [self.deep_input_dims] + config.deep_layer_dim + [1] init_acts = [('Wide_b', [1], config.emb_init)] var_map = init_var_dict(config.init_args, init_acts) self.wide_b = var_map["Wide_b"] self.dense_layer_1 = DenseLayer(self.all_dim_list[0], self.all_dim_list[1], config.weight_bias_init, config.deep_layer_act, convert_dtype=True, drop_out=config.dropout_flag) self.dense_layer_2 = DenseLayer(self.all_dim_list[1], self.all_dim_list[2], config.weight_bias_init, config.deep_layer_act, convert_dtype=True, drop_out=config.dropout_flag) self.dense_layer_3 = DenseLayer(self.all_dim_list[2], self.all_dim_list[3], config.weight_bias_init, config.deep_layer_act, convert_dtype=True, drop_out=config.dropout_flag) self.dense_layer_4 = DenseLayer(self.all_dim_list[3], self.all_dim_list[4], config.weight_bias_init, config.deep_layer_act, convert_dtype=True, drop_out=config.dropout_flag) self.dense_layer_5 = DenseLayer(self.all_dim_list[4], self.all_dim_list[5], config.weight_bias_init, config.deep_layer_act, use_activation=False, convert_dtype=True, drop_out=config.dropout_flag) self.wide_mul = ops.Mul() self.deep_mul = ops.Mul() self.reduce_sum = ops.ReduceSum(keep_dims=False) self.reshape = ops.Reshape() self.deep_reshape = ops.Reshape() self.square = ops.Square() self.concat = ops.Concat(axis=1) self.unique = ops.Unique().shard(((1,),)) self.wide_gatherv2 = ops.Gather() self.deep_gatherv2 = ops.Gather() if is_auto_parallel and sparse and not config.field_slice and not parameter_server: target = 'CPU' if host_device_mix else 'DEVICE' self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, target=target, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE) if config.deep_table_slice_mode == "column_slice": self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target=target, slice_mode=nn.EmbeddingLookup.TABLE_COLUMN_SLICE) if config.use_sp: self.dense_layer_1.matmul.shard(((1, get_group_size()), (get_group_size(), 1))) self.dense_layer_1.bias_add.shard(((get_group_size(), 1), (1,))) self.deep_mul.shard(((1, 1, get_group_size()), (1, 1, 1))) else: self.dense_layer_1.dropout.dropout.shard(((1, get_group_size()),)) self.dense_layer_1.matmul.shard(((1, get_group_size()), (get_group_size(), 1))) self.deep_mul.shard(((1, 1, get_group_size()), (1, 1, 1))) self.dense_layer_1.matmul.add_prim_attr("field_size", self.field_size) self.deep_reshape.add_prim_attr("skip_redistribution", True) else: self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target=target, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE) self.reduce_sum.add_prim_attr("cross_batch", True) self.embedding_table = self.deep_embeddinglookup.embedding_table elif is_auto_parallel and host_device_mix and config.field_slice and config.full_batch and config.manual_shape: manual_shapes = tuple((s[0] for s in config.manual_shape)) self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, slice_mode=nn.EmbeddingLookup.FIELD_SLICE, manual_shapes=manual_shapes) self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, slice_mode=nn.EmbeddingLookup.FIELD_SLICE, manual_shapes=manual_shapes) self.deep_mul.shard(((1, get_group_size(), 1), (1, get_group_size(), 1))) self.wide_mul.shard(((1, get_group_size(), 1), (1, get_group_size(), 1))) self.reduce_sum.shard(((1, get_group_size(), 1),)) self.dense_layer_1.dropout.dropout.shard(((1, get_group_size()),)) self.dense_layer_1.matmul.shard(((1, get_group_size()), (get_group_size(), 1))) self.embedding_table = self.deep_embeddinglookup.embedding_table elif parameter_server: cache_enable = config.vocab_cache_size > 0 target = 'DEVICE' if cache_enable else 'CPU' if not cache_enable: sparse = True if is_auto_parallel and config.full_batch and cache_enable: self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target=target, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE, sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, target=target, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE, sparse=sparse, vocab_cache_size=config.vocab_cache_size) else: self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target=target, sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, target=target, sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.embedding_table = self.deep_embeddinglookup.embedding_table self.deep_embeddinglookup.embedding_table.set_param_ps() self.wide_embeddinglookup.embedding_table.set_param_ps() else: self.deep_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, self.emb_dim, target='DEVICE', sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.wide_embeddinglookup = nn.EmbeddingLookup(config.vocab_size, 1, target='DEVICE', sparse=sparse, vocab_cache_size=config.vocab_cache_size) self.embedding_table = self.deep_embeddinglookup.embedding_table def construct(self, id_hldr, wt_hldr): """ Args: id_hldr: batch ids; wt_hldr: batch weights; """ # Wide layer wide_id_weight = self.wide_embeddinglookup(id_hldr) # Deep layer deep_id_embs = self.deep_embeddinglookup(id_hldr) mask = self.reshape(wt_hldr, (self.batch_size, self.field_size, 1)) # Wide layer wx = self.wide_mul(wide_id_weight, mask) wide_out = self.reshape(self.reduce_sum(wx, 1) + self.wide_b, (-1, 1)) # Deep layer vx = self.deep_mul(deep_id_embs, mask) deep_in = self.deep_reshape(vx, (-1, self.field_size * self.emb_dim)) deep_in = self.dense_layer_1(deep_in) deep_in = self.dense_layer_2(deep_in) deep_in = self.dense_layer_3(deep_in) deep_in = self.dense_layer_4(deep_in) deep_out = self.dense_layer_5(deep_in) out = wide_out + deep_out return out, self.embedding_table class NetWithLossClass(nn.Cell): """" Provide WideDeep training loss through network. Args: network (Cell): The training network config (Class): WideDeep config """ def __init__(self, network, config): super(NetWithLossClass, self).__init__(auto_prefix=False) host_device_mix = bool(config.host_device_mix) parameter_server = bool(config.parameter_server) sparse = config.sparse parallel_mode = context.get_auto_parallel_context("parallel_mode") is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) self.no_l2loss = (is_auto_parallel if (host_device_mix or config.field_slice) else parameter_server) if sparse: self.no_l2loss = True self.network = network self.l2_coef = config.l2_coef self.loss = ops.SigmoidCrossEntropyWithLogits() self.square = ops.Square() self.reduceMean_false = ops.ReduceMean(keep_dims=False) if is_auto_parallel: self.reduceMean_false.add_prim_attr("cross_batch", True) self.reduceSum_false = ops.ReduceSum(keep_dims=False) def construct(self, batch_ids, batch_wts, label): ''' Construct NetWithLossClass ''' predict, embedding_table = self.network(batch_ids, batch_wts) log_loss = self.loss(predict, label) wide_loss = self.reduceMean_false(log_loss) if self.no_l2loss: deep_loss = wide_loss else: l2_loss_v = self.reduceSum_false(self.square(embedding_table)) / 2 deep_loss = self.reduceMean_false(log_loss) + self.l2_coef * l2_loss_v return wide_loss, deep_loss class IthOutputCell(nn.Cell): def __init__(self, network, output_index): super(IthOutputCell, self).__init__() self.network = network self.output_index = output_index def construct(self, x1, x2, x3): predict = self.network(x1, x2, x3)[self.output_index] return predict class TrainStepWrap(nn.Cell): """ Encapsulation class of WideDeep network training. Append Adam and FTRL optimizers to the training network after that construct function can be called to create the backward graph. Args: network (Cell): The training network. Note that loss function should have been added. sens (Number): The adjust parameter. Default: 1024.0 host_device_mix (Bool): Whether run in host and device mix mode. Default: False parameter_server (Bool): Whether run in parameter server mode. Default: False """ def __init__(self, network, sens=1024.0, host_device_mix=False, parameter_server=False, sparse=False, cache_enable=False): super(TrainStepWrap, self).__init__() parallel_mode = context.get_auto_parallel_context("parallel_mode") is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) self.network = network self.network.set_train() self.trainable_params = network.trainable_params() weights_w = [] weights_d = [] for params in self.trainable_params: if 'wide' in params.name: weights_w.append(params) else: weights_d.append(params) self.weights_w = ParameterTuple(weights_w) self.weights_d = ParameterTuple(weights_d) if (sparse and is_auto_parallel) or (sparse and parameter_server): self.optimizer_d = Adam( self.weights_d, learning_rate=5e-4, eps=1e-8, loss_scale=sens, use_lazy=True) self.optimizer_w = FTRL(learning_rate=1e-3, params=self.weights_w, l1=1e-8, l2=1e-8, initial_accum=1.0, loss_scale=sens) if host_device_mix or (parameter_server and not cache_enable): self.optimizer_w.target = "CPU" self.optimizer_d.target = "CPU" else: self.optimizer_d = Adam( self.weights_d, learning_rate=5e-4, eps=1e-8, loss_scale=sens) self.optimizer_w = FTRL(learning_rate=1e-3, params=self.weights_w, l1=1e-8, l2=1e-8, initial_accum=1.0, loss_scale=sens) self.hyper_map = ops.HyperMap() self.grad_w = ops.GradOperation(get_by_list=True, sens_param=True) self.grad_d = ops.GradOperation(get_by_list=True, sens_param=True) self.sens = sens self.loss_net_w = IthOutputCell(network, output_index=0) self.loss_net_d = IthOutputCell(network, output_index=1) self.loss_net_w.set_grad() self.loss_net_d.set_grad() self.reducer_flag = False self.grad_reducer_w = None self.grad_reducer_d = None self.reducer_flag = parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL) if self.reducer_flag: mean = context.get_auto_parallel_context("gradients_mean") degree = context.get_auto_parallel_context("device_num") self.grad_reducer_w = DistributedGradReducer(self.optimizer_w.parameters, mean, degree) self.grad_reducer_d = DistributedGradReducer(self.optimizer_d.parameters, mean, degree) def construct(self, batch_ids, batch_wts, label): ''' Construct wide and deep model ''' weights_w = self.weights_w weights_d = self.weights_d loss_w, loss_d = self.network(batch_ids, batch_wts, label) sens_w = ops.Fill()(ops.DType()(loss_w), ops.Shape()(loss_w), self.sens) sens_d = ops.Fill()(ops.DType()(loss_d), ops.Shape()(loss_d), self.sens) grads_w = self.grad_w(self.loss_net_w, weights_w)(batch_ids, batch_wts, label, sens_w) grads_d = self.grad_d(self.loss_net_d, weights_d)(batch_ids, batch_wts, label, sens_d) if self.reducer_flag: grads_w = self.grad_reducer_w(grads_w) grads_d = self.grad_reducer_d(grads_d) return ops.depend(loss_w, self.optimizer_w(grads_w)), ops.depend(loss_d, self.optimizer_d(grads_d)) class PredictWithSigmoid(nn.Cell): """ Predict definition """ def __init__(self, network): super(PredictWithSigmoid, self).__init__() self.network = network self.sigmoid = ops.Sigmoid() parallel_mode = context.get_auto_parallel_context("parallel_mode") full_batch = context.get_auto_parallel_context("full_batch") is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) if is_auto_parallel and full_batch: self.sigmoid.shard(((1, 1),)) def construct(self, batch_ids, batch_wts, labels): logits, _, = self.network(batch_ids, batch_wts) pred_probs = self.sigmoid(logits) return logits, pred_probs, labels # Pre processing def pre_process_criteo_wide_deep(x): return x class WideDeepPostProcess: def __init__(self): self.good = 0 self.total = 0 self.roc_auc = 0 self.results = [] self.labels = [] def __call__(self, results, expected=None, result_dict=None): processed_results = [] n = len(results) for idx in range(0, n): result = results['auc'] processed_results.append(result) self.good += 1 self.total += 1 return processed_results def add_results(self, labels, results): self.results.append(results) self.labels.append(labels) def start(self): self.good = 0 self.total = 0 self.roc_auc = 0 self.results = [] def finalize(self, result_dict, ds=False, output_dir=None): result_dict["good"] = self.good result_dict["total"] = self.total 这段代码的作用是什么？

-提供了几个函数：_get_h5_dataset,_padding_func,_get_tf_dataset,_get_mindrecord_dataset,_get_vocab_size,compute_manual_shape,create_dataset。-最后定义了一个Dataset类，这个类似乎是整个数据加载流程的封装...

# architectures/base_model.py import torch import inspect import torch.nn as nn from abc import ABC, abstractmethod from components import * import os from datetime import datetime class BaseMultiArchModel(ABC, nn.Module): """多架构模型基类，定义通用组件和接口""" # 组件白名单（核心定型后仅创建者可修改） # 注意：必须注册具体实现类，而非抽象基类！ ALLOWED_COMPONENTS = { "embedding_Standard": StandardEmbedding, # 具体实现类 "embedding_Position": PositionalEmbedding, # 具体实现类 "attention_Self": SelfAttentionModule, # 具体实现类 "attention_Dynamic": DynamicAttention, # 具体实现类 "norm_Layer": LayerNormModule, # 具体实现类 "norm_Dynamic": DynamicNormModule, # 具体实现类 "cnn_Conv1d": Conv1dModule, # 具体实现类 "cnn_MultiK": MultiKernelCNN, # 具体实现类 "rnn_GRU": GRUModule, # 具体实现类 "rnn_LSTM": LSTMModule, # 具体实现类 "transformer_Transformer": TransformerModule, # 具体实现类 "transformer_Lightweight": LightweightTransformer, # 具体实现类 "fusion_Conca": ConcatFusion, # 具体实现类 "fusion_Attention": AttentionFusion # 具体实现类 } # 基因快照配置 GENE_SNAPSHOT_DIR = "gene_snapshots/" def __init__(self, model_config): super().__init__() self.model_config = model_config self._init_components() self._ensure_snapshot_dir() def _init_components(self): """初始化通用组件（增加白名单校验）""" # 创建嵌入层（任务特定，需子类实现） self.embedding = self._create_embedding() # 创建通用组件（使用白名单校验） for comp_type in [ "embedding_Standard", "embedding_Position", "attention_Self", "attention_Dynamic", "norm_Layer", "norm_Dynamic", "cnn_Conv1d", "cnn_MultiK", "rnn_GRU", "rnn_LSTM", "transformer_Transformer", "transformer_Lightweight", "fusion_Conca", "fusion_Attention" ]: config = self.model_config["model_components"].get(comp_type, {}) comp_class = self.ALLOWED_COMPONENTS[comp_type] # 校验组件是否被篡改 assert issubclass(comp_class, BaseComponent), f"非法组件：{comp_type}" # 确保是具体实现类 assert not inspect.isabstract(comp_class), f"非法组件：{comp_type} 是抽象类" # 创建组件实例 setattr(self, comp_type, comp_class(**config)) # 【修改】根据配置设置默认组件的快捷引用 # 不再通过命名规则自动推断，而是通过配置文件显式指定 default_components = self.model_config.get("default_components", {}) # 默认组件映射关系 default_mapping = { "attention": "attention_Self", "norm": "norm_Layer", "cnn": "cnn_Conv1d", "rnn": "rnn_GRU", "transformer": "transformer_Transformer", "fusion": "fusion_Conca" } # 应用默认组件配置 for attr_name, default_key in default_mapping.items(): # 如果配置中指定了其他组件，则使用配置中的组件 config_key = default_components.get(attr_name, default_key) # 检查组件是否存在 if not hasattr(self, config_key): raise KeyError(f"默认组件 {config_key} 未在组件初始化中创建，请检查配置！") # 设置快捷引用，例如 self.attention = self.attention_Self setattr(self, attr_name, getattr(self, config_key)) def _ensure_snapshot_dir(self): """确保基因快照目录存在""" if not os.path.exists(self.GENE_SNAPSHOT_DIR): os.makedirs(self.GENE_SNAPSHOT_DIR) @abstractmethod def _create_embedding(self): """创建任务特定的嵌入层""" pass @abstractmethod def _create_fusion(self): """创建特征融合层""" pass # 通用组件创建方法（保留，但修改为使用配置中指定的组件） def _create_attention(self): # 从配置中获取使用哪个注意力组件 attention_type = self.model_config["model_components"].get("attention_type", "attention_Self") config = self.model_config["model_components"].get(attention_type, {}) return self.ALLOWED_COMPONENTS[attention_type]( embed_dim=self.model_config["embedding_dim"], **config ) def _create_normalization(self): # 从配置中获取使用哪个归一化组件 norm_type = self.model_config["model_components"].get("norm_type", "norm_Layer") config = self.model_config["model_components"].get(norm_type, {}) return self.ALLOWED_COMPONENTS[norm_type]( num_features=self.model_config["embedding_dim"], **config ) def _create_cnn(self): # 从配置中获取使用哪个CNN组件 cnn_type = self.model_config["model_components"].get("cnn_type", "cnn_Conv1d") config = self.model_config["model_components"].get(cnn_type, {}) return self.ALLOWED_COMPONENTS[cnn_type]( in_channels=self.model_config["embedding_dim"], out_channels=self.model_config["hidden_dim"], **config ) def _create_rnn(self): # 从配置中获取使用哪个RNN组件 rnn_type = self.model_config["model_components"].get("rnn_type", "rnn_GRU") config = self.model_config["model_components"].get(rnn_type, {}) return self.ALLOWED_COMPONENTS[rnn_type]( input_size=self.model_config["embedding_dim"], hidden_size=self.model_config["hidden_dim"], **config ) def _create_transformer(self): # 从配置中获取使用哪个Transformer组件 transformer_type = self.model_config["model_components"].get("transformer_type", "transformer_Transformer") config = self.model_config["model_components"].get(transformer_type, {}) return self.ALLOWED_COMPONENTS[transformer_type]( d_model=self.model_config["embedding_dim"], **config ) def forward_features(self, x): """通用特征提取流程""" # 嵌入层处理 x_embed = self.embedding(x) # 使用默认的归一化和注意力组件 x_embed = self.norm(x_embed) if hasattr(self, 'norm') else x_embed x_embed = self.attention(x_embed) if hasattr(self, 'attention') else x_embed # 多架构特征提取 cnn_out = self.cnn(x_embed.transpose(1, 2)).transpose(1, 2) if hasattr(self, 'cnn') else None rnn_out = self.rnn(x_embed) if hasattr(self, 'rnn') else None trans_out = self.transformer(x_embed) if hasattr(self, 'transformer') else None # 特征融合 fusion_inputs = {} if cnn_out is not None: fusion_inputs["cnn"] = cnn_out if rnn_out is not None: fusion_inputs["rnn"] = rnn_out if trans_out is not None: fusion_inputs["transformer"] = trans_out fused_features = self.fusion(fusion_inputs) return fused_features @abstractmethod def forward(self, x): """主前向传播方法""" pass def save_gene_snapshot(self, snapshot_name=None, evolution_note=""): """保存核心组件权重（基因快照）""" snapshot_name = snapshot_name or datetime.now().strftime("%Y%m%d_%H%M%S") snapshot_path = os.path.join(self.GENE_SNAPSHOT_DIR, f"{snapshot_name}.pth") # 保存所有组件的权重 state_dict = { "model_config": self.model_config } # 保存主要组件的权重 for attr in ["embedding", "attention", "norm", "cnn", "rnn", "transformer", "fusion"]: if hasattr(self, attr): state_dict[attr] = getattr(self, attr).state_dict() # 保存所有注册的组件权重 for comp_type in self.ALLOWED_COMPONENTS.keys(): if hasattr(self, comp_type): state_dict[comp_type] = getattr(self, comp_type).state_dict() torch.save(state_dict, snapshot_path) # 写入进化日志 with open(os.path.join(self.GENE_SNAPSHOT_DIR, "evolution_log.md"), "a") as f: f.write(f"## {snapshot_name}\n") f.write(f"变更原因：{evolution_note}\n") f.write(f"时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n") def load_gene_snapshot(self, snapshot_path): """加载基因快照（复活/回退用）""" state_dict = torch.load(snapshot_path) # 更新配置 self.model_config = state_dict["model_config"] # 加载主要组件权重 for attr in ["embedding", "attention", "norm", "cnn", "rnn", "transformer", "fusion"]: if attr in state_dict and hasattr(self, attr): getattr(self, attr).load_state_dict(state_dict[attr]) # 加载所有注册的组件权重 for comp_type in self.ALLOWED_COMPONENTS.keys(): if comp_type in state_dict and hasattr(self, comp_type): getattr(self, comp_type).load_state_dict(state_dict[comp_type]) print(f"[萧默芯] 成功加载基因快照: {snapshot_path}") 帮我分析一下这个代码的可靠性和能力！

for param in self.embedding.parameters(): param.requires_grad = False # 冻结嵌入层 --- ### **二、功能能力评估** 1. **模块兼容性** - **支持能力**： | **组件类型** | **关键验证点** | |...

import torch import torch.nn as nn import torch.nn.init as init from TransformerBlock import MultiheadAttention from .NeuralNetwork import NeuralNetwork import torch.nn.functional as F from .GAT import GATConv import torch_geometric.utils as utils class Attention(nn.Module): def __init__(self, in_features, hidden_size): super(Attention, self).__init__() self.linear1 = nn.Linear(in_features*2, hidden_size) self.linear2 = nn.Linear(hidden_size, 1) self.activation = nn.ReLU() self.dropout = nn.Dropout(0.5) self.reset_parameters() def reset_parameters(self): init.xavier_normal_(self.linear1.weight) init.xavier_normal_(self.linear2.weight) def forward(self, K, V, mask = None): ''' :param K: (batch_size, d) :param V: (batch_size, hist_len, d) :return: (batch_size, d) ''' K = K.unsqueeze(dim=1).expand(V.size()) fusion = torch.cat([K, V], dim=-1) fc1 = self.activation(self.linear1(fusion)) score = self.linear2(fc1) if mask is not None: mask = mask.unsqueeze(dim=-1) score = score.masked_fill(mask, -2 ** 32 + 1) alpha = F.softmax(score, dim=1) alpha = self.dropout(alpha) att = (alpha * V).sum(dim=1) return att class GLAN(NeuralNetwork): def __init__(self, config, graph): super(GLAN, self).__init__() self.config = config embedding_weights = config['embedding_weights'] V, D = embedding_weights.shape maxlen = config['maxlen'] dropout_rate = config['dropout'] alpha = 0.4 self.graph = graph self.word_embedding = nn.Embedding(V, D, padding_idx=0, _weight=torch.from_numpy(embedding_weights)) self.user_tweet_embedding = nn.Embedding(graph.num_nodes, 300, padding_idx=0) self.mh_attention = MultiheadAttention(input_size=300, output_size=300) self.linear_fuse = nn.Lin

def __init__(self, d_model, num_heads): super().__init__() self.d_model = d_model self.num_heads = num_heads self.d_k = d_model // num_heads self.W_Q = nn.Linear(d_model, d_model) self.W_K = ...

代码出现问题：(style_tune) C:\Users\28996\Desktop\AI\persona_contrastive_finetuning>python Contrastive_Training_LM.py INFO:accelerate.utils.modeling:We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set max_memory in to a higher value to use more memory (at your own risk). trainable params: 1,572,864 || all params: 1,838,401,536 || trainable%: 0.0856 训练集样本示例: {'anchor_input_ids': [56568, 118919, 116122, 11319], 'positive_input_ids': [116122, 20412, 107340, 9370, 100357, 102323, 3837, 109202, 104078, 103975, 100675, 101940, 100912, 105054, 6313], 'negative_input_ids': [100323, 104307, 99245, 9370, 106059, 104060, 3837, 104530, 115604, 99329, 11319]} 验证集样本示例: {'anchor_input_ids': [56568, 118919, 116122, 11319], 'positive_input_ids': [116122, 20412, 107340, 9370, 100357, 102323, 3837, 109202, 104078, 103975, 100675, 101940, 100912, 105054, 6313], 'negative_input_ids': [100323, 104307, 99245, 9370, 106059, 104060, 3837, 104530, 115604, 99329, 11319]} Trainer.tokenizer is now deprecated. You should use Trainer.processing_class = processing_class instead. INFO:__main__:GPU内存使用: 已分配 2.93GB, 保留 4.13GB 可训练参数列表: - base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.0.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.0.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.0.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.1.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.1.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.1.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.1.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.2.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.2.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.2.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.2.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.3.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.3.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.3.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.3.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.4.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.4.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.4.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.4.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.5.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.5.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.5.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.5.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.6.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.6.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.6.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.6.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.7.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.7.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.7.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.7.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.8.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.8.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.8.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.8.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.9.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.9.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.9.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.9.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.10.self_attn.q_proj.lora_A.default.weight - 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base_model.model.model.layers.20.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.21.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.21.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.21.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.21.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.22.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.22.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.22.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.22.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.23.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.23.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.23.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.23.self_attn.v_proj.lora_B.default.weight 0%| | 0/3 [00:00<?, ?it/s]You're using a Qwen2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the __call__ method is faster than using a method to encode the text followed by a call to the pad method to get a padded encoding. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.00GB, 保留 4.21GB Could not estimate the number of tokens of the input, floating-point operations will not be computed Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.02GB, 保留 4.22GB 33%|████████████████████████████ | 1/3 [00:03<00:06, 3.25s/it]Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.01GB, 保留 4.25GB Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.02GB, 保留 4.26GB 67%|████████████████████████████████████████████████████████ | 2/3 [00:06<00:02, 2.98s/it]Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.01GB, 保留 4.25GB Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.02GB, 保留 4.26GB {'train_runtime': 9.034, 'train_samples_per_second': 0.664, 'train_steps_per_second': 0.332, 'train_loss': 1.0772175788879395, 'epoch': 3.0} 100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:09<00:00, 3.01s/it] Traceback (most recent call last): File "C:\Users\28996\Desktop\AI\persona_contrastive_finetuning\Contrastive_Training_LM.py", line 356, in <module> eval_results = trainer.evaluate() File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 4076, in evaluate output = eval_loop( File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 4270, in evaluation_loop losses, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 4496, in prediction_step outputs = model(**inputs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1736, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1747, in _call_impl return forward_call(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\accelerate\utils\operations.py", line 818, in forward return model_forward(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\accelerate\utils\operations.py", line 806, in __call__ return convert_to_fp32(self.model_forward(*args, **kwargs)) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\amp\autocast_mode.py", line 44, in decorate_autocast return func(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\peft\peft_model.py", line 1719, in forward return self.base_model( File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1736, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1747, in _call_impl return forward_call(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\peft\tuners\tuners_utils.py", line 197, in forward return self.model.forward(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\models\qwen2\modeling_qwen2.py", line 816, in forward outputs = self.model( File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1736, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1747, in _call_impl return forward_call(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\models\qwen2\modeling_qwen2.py", line 521, in forward raise ValueError("You must specify exactly one of input_ids or inputs_embeds") ValueError: You must specify exactly one of input_ids or inputs_embeds (style_tune) C:\Users\28996\Desktop\AI\persona_contrastive_finetuning>python Contrastive_Training_LM.py Traceback (most recent call last): File "C:\Users\28996\Desktop\AI\persona_contrastive_finetuning\Contrastive_Training_LM.py", line 57, in <module> class ContrastiveTrainer(Trainer): File "C:\Users\28996\Desktop\AI\persona_contrastive_finetuning\Contrastive_Training_LM.py", line 63, in ContrastiveTrainer eval_dataset: Optional[Dataset] = None, NameError: name 'Dataset' is not defined 原代码如下：import torch import torch.nn as nn import torch.nn.functional as F from transformers import ( AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, PreTrainedTokenizerBase, BitsAndBytesConfig ) from transformers.tokenization_utils_base import PreTrainedTokenizerBase from transformers.utils import PaddingStrategy from datasets import load_dataset from typing import Any, Dict, List, Optional, Tuple, Union import logging from dataclasses import dataclass import os import gc from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training @dataclass class EvalDataCollator: """评估专用的数据收集器""" tokenizer: PreTrainedTokenizerBase padding: Union[bool, str, PaddingStrategy] = True max_length: Optional[int] = None pad_to_multiple_of: Optional[int] = None return_tensors: str = "pt" def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]: # 评估时只使用正样本（用于语言建模评估） positive_features = [{"input_ids": f["positive_input_ids"]} for f in features] # 对正样本进行填充 batch_positive = self.tokenizer.pad( positive_features, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors=self.return_tensors, ) # 创建注意力掩码 attention_mask = (batch_positive["input_ids"] != self.tokenizer.pad_token_id).int() # 创建标签（用于语言建模） labels = batch_positive["input_ids"].clone() labels[labels == self.tokenizer.pad_token_id] = -100 return { "input_ids": batch_positive["input_ids"], "attention_mask": attention_mask, "labels": labels } class ContrastiveTrainer(Trainer): """内存优化的训练器""" # ... [保持其他方法不变] ... def evaluate( self, eval_dataset: Optional[Dataset] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "eval", ) -> Dict[str, float]: """重写评估方法以使用专用的数据收集器""" # 创建评估专用的数据收集器 eval_data_collator = EvalDataCollator( tokenizer=self.tokenizer, max_length=256, padding="max_length" ) # 临时保存原始数据收集器 original_collator = self.data_collator try: # 使用评估专用的数据收集器 self.data_collator = eval_data_collator # 调用父类的评估方法 return super().evaluate( eval_dataset=eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix ) finally: # 恢复原始数据收集器 self.data_collator = original_collator # 设置日志 logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # 内存优化工具函数 def clear_memory(): """清除Python和CUDA缓存""" gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.reset_peak_memory_stats() def print_memory_usage(): """打印当前内存使用情况""" if torch.cuda.is_available(): allocated = torch.cuda.memory_allocated() / (1024 ** 3) reserved = torch.cuda.memory_reserved() / (1024 ** 3) logger.info(f"GPU内存使用: 已分配 {allocated:.2f}GB, 保留 {reserved:.2f}GB") else: logger.info("未检测到GPU") def tokenize_function(examples, tokenizer, max_length=256): """将文本转换为token IDs""" tokenized = {} # 对每个字段进行分词 for key in ['anchor', 'positive', 'negative']: if key in examples: # 使用分词器处理文本 result = tokenizer( examples[key], max_length=max_length, truncation=True, padding=False, return_tensors=None ) tokenized[f"{key}_input_ids"] = result["input_ids"] return tokenized @dataclass class ContrastiveDataCollator: """内存优化的数据收集器""" tokenizer: PreTrainedTokenizerBase padding: Union[bool, str, PaddingStrategy] = True max_length: Optional[int] = None pad_to_multiple_of: Optional[int] = None return_tensors: str = "pt" def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]: # 分离出三元组的各个部分 anchor_features = [{"input_ids": f["anchor_input_ids"]} for f in features] positive_features = [{"input_ids": f["positive_input_ids"]} for f in features] negative_features = [{"input_ids": f["negative_input_ids"]} for f in features] # 对每个部分分别进行填充 batch_anchor = self.tokenizer.pad( anchor_features, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors=self.return_tensors, ) batch_positive = self.tokenizer.pad( positive_features, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors=self.return_tensors, ) batch_negative = self.tokenizer.pad( negative_features, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors=self.return_tensors, ) # 创建注意力掩码 def create_attention_mask(input_ids): return (input_ids != self.tokenizer.pad_token_id).int() # 释放中间变量内存 del anchor_features, positive_features, negative_features clear_memory() return { "anchor_input_ids": batch_anchor["input_ids"], "anchor_attention_mask": create_attention_mask(batch_anchor["input_ids"]), "positive_input_ids": batch_positive["input_ids"], "positive_attention_mask": create_attention_mask(batch_positive["input_ids"]), "negative_input_ids": batch_negative["input_ids"], "negative_attention_mask": create_attention_mask(batch_negative["input_ids"]), } class ContrastiveTrainer(Trainer): """内存优化的训练器""" def __init__(self, tokenizer=None, *args, contrastive_config=None, **kwargs): # 首先调用父类初始化 super().__init__(*args, **kwargs) # 关键修复：设置tokenizer self.tokenizer = tokenizer if contrastive_config is None: contrastive_config = {} # 设置默认值 self.temperature = contrastive_config.get("temperature", 0.07) self.margin = contrastive_config.get("margin", 0.3) self.contrastive_weight = contrastive_config.get("weight", 0.8) self.repr_layer = contrastive_config.get("repr_layer", -1) # 验证必要参数 if not hasattr(self.model.config, "output_hidden_states") or not self.model.config.output_hidden_states: raise ValueError("模型必须设置output_hidden_states=True") self.cross_entropy = nn.CrossEntropyLoss() def compute_contrastive_loss(self, anchor_emb, pos_emb, neg_emb): """计算对比损失""" # 计算余弦相似度 pos_sim = F.cosine_similarity(anchor_emb, pos_emb) neg_sim = F.cosine_similarity(anchor_emb, neg_emb) # 计算InfoNCE损失 numerator = torch.exp(pos_sim / self.temperature) denominator = numerator + torch.exp(neg_sim / self.temperature) info_nce_loss = -torch.log(numerator / (denominator + 1e-8)).mean() # 计算三元组损失 triplet_loss = F.relu(neg_sim - pos_sim + self.margin).mean() return info_nce_loss + triplet_loss def get_sequence_representation(self, outputs, attention_mask): """获取序列表示（内存优化版）""" # 只获取需要的隐藏状态层 hidden_states = outputs.hidden_states[self.repr_layer] # 获取每个序列的最后一个非填充token seq_lengths = attention_mask.sum(dim=1) - 1 batch_indices = torch.arange(hidden_states.size(0)) # 返回对应位置的隐藏状态 return hidden_states[batch_indices, seq_lengths] def compute_loss(self, model, inputs, return_outputs=False): """内存优化的损失计算""" # 确保模型处于训练模式 model.train() # 提取输入 anchor_ids = inputs["anchor_input_ids"] anchor_mask = inputs["anchor_attention_mask"] positive_ids = inputs["positive_input_ids"] positive_mask = inputs["positive_attention_mask"] negative_ids = inputs["negative_input_ids"] negative_mask = inputs["negative_attention_mask"] # 前向传播获取隐藏状态 def get_embeddings(input_ids, attention_mask): outputs = model( input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=True, return_dict=True ) return self.get_sequence_representation(outputs, attention_mask) # 获取三元组的嵌入表示 anchor_emb = get_embeddings(anchor_ids, anchor_mask) pos_emb = get_embeddings(positive_ids, positive_mask) neg_emb = get_embeddings(negative_ids, negative_mask) # 计算对比损失 cl_loss = self.compute_contrastive_loss(anchor_emb, pos_emb, neg_emb) cl_loss = cl_loss * self.contrastive_weight # 关键修复：确保tokenizer已设置 if self.tokenizer is None: raise ValueError("Tokenizer未设置！") # 计算语言建模损失 lm_labels = positive_ids.clone() # 关键修复：使用tokenizer的pad_token_id pad_token_id = self.tokenizer.pad_token_id lm_labels[lm_labels == pad_token_id] = -100 # 计算语言建模损失 lm_outputs = model( input_ids=positive_ids, attention_mask=positive_mask, labels=lm_labels ) lm_loss = lm_outputs.loss # 总损失 = LM损失 + 对比损失 total_loss = lm_loss + cl_loss # 记录内存使用 print_memory_usage() return (total_loss, lm_outputs) if return_outputs else total_loss # ================ 主程序 ================ # if __name__ == "__main__": # 配置量化以减少内存使用 bnb_config = BitsAndBytesConfig( load_in_4bit=True, # 使用4位量化 bnb_4bit_quant_type="nf4", # 使用NF4量化类型 bnb_4bit_use_double_quant=True, # 双重量化 bnb_4bit_compute_dtype=torch.float16 # 计算使用FP16 ) # 加载模型和分词器（使用量化） model = AutoModelForCausalLM.from_pretrained( "model/Qwen/Qwen1.5-1.8B", quantization_config=bnb_config, # 应用量化配置 device_map="auto", # 自动选择设备 output_hidden_states=True, # 必须设置以获取隐藏状态 return_dict_in_generate=True, use_cache=False # 禁用缓存以节省内存 ) tokenizer = AutoTokenizer.from_pretrained("model/Qwen/Qwen1.5-1.8B") tokenizer.pad_token = tokenizer.eos_token # 设置填充token # 为量化模型添加LoRA适配器 lora_config = LoraConfig( r=8, lora_alpha=32, target_modules=["q_proj", "v_proj"], # 针对Qwen1.5-1.8B模型 lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) # 关键修复：准备模型用于k位训练 model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True) # 添加LoRA适配器 model = get_peft_model(model, lora_config) # 关键修复：显式启用LoRA参数的梯度 for param in model.parameters(): if param.requires_grad: param.requires_grad = True model.print_trainable_parameters() # 打印可训练参数数量 # 加载数据集 def load_and_tokenize_dataset(file_path, tokenizer): """加载数据集并进行分词处理""" # 加载原始数据集 dataset_dict = load_dataset('json', data_files=file_path) raw_dataset = dataset_dict['train'] # 应用分词函数 tokenized_dataset = raw_dataset.map( lambda ex: tokenize_function(ex, tokenizer, max_length=256), batched=True, batch_size=8, # 减小批处理大小 remove_columns=['anchor', 'positive', 'negative'] ) return tokenized_dataset train_dataset = load_and_tokenize_dataset('data/processed/train_style_triplets.json', tokenizer) val_dataset = load_and_tokenize_dataset('data/processed/val_style_triplets.json', tokenizer) # 验证数据集格式 print("训练集样本示例:", train_dataset[0]) print("验证集样本示例:", val_dataset[0]) # 训练参数配置（内存优化） training_args = TrainingArguments( output_dir="./model/lora_adapter", per_device_train_batch_size=1, # 减小批量大小 gradient_accumulation_steps=8, # 增加梯度累积步数 num_train_epochs=3, learning_rate=2e-4, logging_steps=10, # 更频繁的日志记录以监控内存 save_steps=500, fp16=True, report_to="none", remove_unused_columns=False, gradient_checkpointing=True, # 启用梯度检查点 optim="adafactor", # 使用内存更少的优化器 ) # 对比学习配置 contrastive_config = { "temperature": 0.07, "margin": 0.3, "weight": 0.8, "repr_layer": -1 } # 初始化数据收集器 data_collator = ContrastiveDataCollator( tokenizer=tokenizer, max_length=256, # 减少最大长度 padding="max_length" ) # 初始化训练器 - 关键修复：传递tokenizer trainer = ContrastiveTrainer( model=model, args=training_args, tokenizer=tokenizer, # 传递tokenizer data_collator=data_collator, train_dataset=train_dataset, eval_dataset=val_dataset, contrastive_config=contrastive_config ) # 开始训练前打印内存状态 print_memory_usage() # 关键修复：验证可训练参数 print("可训练参数列表:") for name, param in model.named_parameters(): if param.requires_grad: print(f"- {name}") # 开始训练 trainer.train() # 保存LoRA适配器 model.save_pretrained("./model/lora_adapter") # 评估模型 try: eval_results = trainer.evaluate() print("评估结果:", eval_results) except Exception as e: print(f"评估过程中发生错误: {e}") import traceback traceback.print_exc()

def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]: # 分离出三元组的各个部分 anchor_features = [{"input_ids": f["anchor_input_ids"]} for f in features] positive_features...

import torch import re import numpy as np from typing import List, Tuple, Dict, Any from transformers import ( AutoTokenizer, PreTrainedModel, AutoConfig, LlamaForCausalLM, GenerationConfig ) import torch.nn as nn from tqdm import tqdm from collections import defaultdict import pandas as pd # -------------------------- # 1. 常量与预处理函数（采用新的数据处理方式） # -------------------------- VALID_ELEMENTS = ["C", "N", "P", "O", "S", "Si", "I", "H", "Cl", "F", "Br", "B", "Se", "Fe", "Co", "As", "K", "Na"] element_to_idx = {elem: idx for idx, elem in enumerate(VALID_ELEMENTS)} CHEM_FORMULA_SIZE = r"([A-Z][a-z]*)([0-9]*)" # 新增的分子公式解析函数 def parse_chem_formula(formula): pattern = r'([A-Z][a-z]?)(\d*)' matches = re.findall(pattern, formula) element_counts = defaultdict(int) for (element, count) in matches: count = int(count) if count else 1 element_counts[element] += count return element_counts def generate_element_list(formula): element_counts = parse_chem_formula(formula) elements = [] for element, count in element_counts.items(): # 跳过氢元素 if element != "H": elements.extend([element] * count) return ''.join(elements) # 化学式转密集向量 def formula_to_dense(chem_formula: str) -> torch.Tensor: dense_vec = torch.zeros(len(VALID_ELEMENTS), dtype=torch.float32) matches = re.findall(CHEM_FORMULA_SIZE, chem_formula) for chem_symbol, num_str in matches: num = 1 if num_str == "" else int(num_str) if chem_symbol in element_to_idx: idx = element_to_idx[chem_symbol] dense_vec[idx] += num return dense_vec # 位置编码生成 (PyTorch实现) def positional_encoding(max_position: int, d_model: int, min_freq: float = 1e-4) -> torch.Tensor: position = torch.arange(max_position).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2) * (-torch.log(torch.tensor(min_freq)) / d_model)) pos_enc = torch.zeros(max_position, d_model) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position * div_term) return pos_enc # 初始化位置编码矩阵 P = positional_encoding(2000000, 254) dimn = 254 # 与位置编码维度一致 # 质谱数据编码 - 优化短数据处理：仅截断过长数据，不填充短数据 def encode_spectra(rag_tensor: list, P: torch.Tensor, dimn: int) -> list: # 返回列表而非堆叠张量 encoded_list = [] max_len = 501 # 仅对过长数据截断，不强制填充短数据 for sample in rag_tensor: mz_list, intensity_list = sample # 创建基础特征矩阵 [m/z, intensity] base_features = torch.tensor([mz_list, intensity_list], dtype=torch.float32).T # 添加位置编码特征（保留原始m/z的位置信息） pos_enc = torch.stack([P[min(int(mz), P.size(0)-1)] for mz in mz_list]) # 组合所有特征 [m/z, intensity, pos_enc...] features = torch.cat([base_features, pos_enc], dim=1) # 仅截断过长数据，短数据保持原始长度（不填充） if features.size(0) > max_len: features = features[:max_len] encoded_list.append(features) # 保留原始长度特征 return encoded_list # 质谱数据预处理 - 确保短数据完整保留 def preprocess_spectra_for_inference(spectrum_str: str, total_mass: float) -> list: # 解析质谱字符串 pairs = spectrum_str.split() mz_list, intensity_list = [], [] for pair in pairs: mz, intensity = pair.split(':') mz_list.append(float(mz)) intensity_list.append(float(intensity)) # 对于仅含一组数据的情况，额外保留原始精度（不四舍五入） if len(pairs) == 1: # 保留原始精度，不进行四舍五入 mz_list = [float(mz) for mz, _ in [pair.split(':') for pair in pairs]] intensity_list = [float(intensity) for _, intensity in [pair.split(':') for pair in pairs]] # 添加总精确质量（作为补充特征，不影响原始数据长度） mz_list.append(total_mass) intensity_list.append(0.0) # 仅对长数据进行四舍五入，短数据保留更多精度 if len(mz_list) > 5: # 数据较长时才简化 mz_list = [round(mz, 2) for mz in mz_list] intensity_list = [round(intensity, 2) for intensity in intensity_list] return [[mz_list, intensity_list]] # -------------------------- # 2. 模型类定义（保持结构，采用新实现） # -------------------------- from transformers.modeling_outputs import CausalLMOutputWithPast from transformers.generation.utils import GenerationMixin class LlamaWithEncoder(PreTrainedModel, GenerationMixin): config_class = AutoConfig _no_split_modules = ["LlamaDecoderLayer", "TransformerEncoderLayer"] def __init__(self, config, base_model=None, encoder1_dim=18, encoder2_dim=256, hidden_dim=512): # 添加config属性 self.config = config super().__init__(self.config) # 如果未提供base_model，则从config初始化 if base_model is None: self.model = LlamaForCausalLM(config) else: self.model = base_model # 第一个Transformer Encoder（处理分子式向量） encoder1_layer = nn.TransformerEncoderLayer( d_model=encoder1_dim, nhead=3, dim_feedforward=hidden_dim, batch_first=True ) self.encoder1 = nn.TransformerEncoder(encoder1_layer, num_layers=2) # 第二个Transformer Encoder（处理质谱矩阵） encoder2_layer = nn.TransformerEncoderLayer( d_model=encoder2_dim, nhead=4, dim_feedforward=hidden_dim, batch_first=True ) self.encoder2 = nn.TransformerEncoder(encoder2_layer, num_layers=2) # 投影层：将编码器输出映射到模型隐藏层维度 self.proj1 = nn.Linear(encoder1_dim, base_model.config.hidden_size) self.proj2 = nn.Linear(encoder2_dim, base_model.config.hidden_size) # 嵌入层（复制基础模型权重但不共享） self.embed_tokens = nn.Embedding( num_embeddings=base_model.config.vocab_size, embedding_dim=base_model.config.hidden_size, padding_idx=base_model.config.pad_token_id ) self.embed_tokens.weight.data = base_model.get_input_embeddings().weight.data.clone() # 必要接口实现 def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def get_output_embeddings(self): return self.model.get_output_embeddings() def set_output_embeddings(self, new_embeddings): self.model.set_output_embeddings(new_embeddings) def get_base_model(self): return self.model def forward( self, input_ids=None, attention_mask=None, encoder1_inputs=None, encoder2_inputs=None, labels=None, past_key_values=None, output_attentions=None, output_hidden_states=None, return_dict=None,** kwargs ) -> CausalLMOutputWithPast: # 1. 编码器处理 # 分子式编码器输出 enc1_out = self.encoder1(encoder1_inputs) # (batch_size, 1, 18) enc1_out = enc1_out.mean(dim=1) # (batch_size, 18) enc1_proj = self.proj1(enc1_out) # (batch_size, hidden_size) # 质谱编码器输出 enc2_out = self.encoder2(encoder2_inputs) # (batch_size, seq_len, 256) enc2_out = enc2_out.mean(dim=1) # (batch_size, 256) enc2_proj = self.proj2(enc2_out) # (batch_size, hidden_size) # 合并编码器输出（用于替换<mask>） mask_replacement = (enc1_proj + enc2_proj) / 2 # (batch_size, hidden_size) # 2. 获取原始嵌入（避免inplace，全程用新张量） embeddings = self.embed_tokens(input_ids) # (batch_size, seq_len, hidden_size) batch_size, seq_len, hidden_size = embeddings.size() # 3. 替换<mask> token（第三个token，索引=2）：用拼接替代inplace赋值 if seq_len > 2: mask_embed = mask_replacement.unsqueeze(1) # (batch_size, 1, hidden_size) # 拆分张量并拼接（前2个token + 替换的mask_embed + 剩余token） part1 = embeddings[:, :2, :] # (batch_size, 2, hidden_size) part2 = mask_embed # (batch_size, 1, hidden_size) part3 = embeddings[:, 3:, :] # (batch_size, seq_len-3, hidden_size) # 拼接为新张量（无inplace操作） new_embeddings = torch.cat([part1, part2, part3], dim=1) # (batch_size, seq_len, hidden_size) else: new_embeddings = embeddings # 序列过短时直接使用原始嵌入 # 4. 调用基础模型 return self.model( inputs_embeds=new_embeddings, attention_mask=attention_mask, labels=labels, past_key_values=past_key_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) def prepare_inputs_for_generation(self, input_ids, **kwargs): return { "input_ids": input_ids, "attention_mask": kwargs.get("attention_mask", None), "encoder1_inputs": kwargs.get("encoder1_inputs", None), "encoder2_inputs": kwargs.get("encoder2_inputs", None), } def _get_generation_device(self): return next(self.parameters()).device # -------------------------- # 3. 加载模型和Tokenizer（修复核心错误） # -------------------------- model_path = "./llama3.2-SELFIES" # 模型保存路径 # 加载分词器 tokenizer = AutoTokenizer.from_pretrained(model_path) # 确保mask token存在 if tokenizer.mask_token is None: tokenizer.add_special_tokens({"mask_token": "<mask>"}) # 加载模型配置 config = AutoConfig.from_pretrained(model_path) # 设备配置（优先使用GPU） device = "cuda:0" if torch.cuda.is_available() else "cpu" # 修复：先加载基础模型，再传入自定义模型 base_model = LlamaForCausalLM.from_pretrained( model_path, config=config, torch_dtype=torch.bfloat16, # 确保基础模型为bfloat16精度 device_map=device ) # 使用基础模型初始化自定义模型 model = LlamaWithEncoder( config=config, base_model=base_model, encoder1_dim=18, encoder2_dim=256, hidden_dim=512 ) model = model.to(device) # 先转移到设备 model.eval() # 推理模式 # -------------------------- # 4. 推理函数（适配新的数据处理方式） # -------------------------- def generate_selfies( formula: str, spectrum_str: str, total_mass: float, max_length: int = 512, temperature: float = 0.7, top_p: float = 0.9 ) -> str: """生成SELFIES字符串""" model_device = next(model.parameters()).device # 1. 生成element_list element_list = generate_element_list(formula) # 2. 处理分子式向量 formula_vec = formula_to_dense(formula).unsqueeze(0).unsqueeze(0) # (1,1,18) formula_vec = formula_vec.to(model_device, dtype=torch.bfloat16) # 3. 处理质谱数据（使用新的预处理和编码方式） spectra_data = preprocess_spectra_for_inference(spectrum_str, total_mass) spec_encoded = encode_spectra(spectra_data, P, dimn) # 得到列表形式的编码结果 spec_matrix = spec_encoded[0].to(model_device, dtype=torch.bfloat16).unsqueeze(0) # 添加批次维度 # 4. 构造输入提示 prompt = f"<｜User｜><｜Spectrum｜>{element_list}" input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model_device) attention_mask = torch.ones_like(input_ids).to(model_device) # 5. 模型生成 with torch.no_grad(): # 关闭梯度计算 outputs = model.generate( input_ids=input_ids, attention_mask=attention_mask, encoder1_inputs=formula_vec, # 分子式特征 encoder2_inputs=spec_matrix, # 质谱特征 max_length=max_length, temperature=temperature, top_p=top_p, ) # 6. 解码生成结果（去除特殊token） generated = tokenizer.decode(outputs[0], skip_special_tokens=False) return generated # -------------------------- # 5. 推理示例 # -------------------------- if __name__ == "__main__": # 示例输入 example_formula = "C9H9N3O2S2" # 分子式 example_spectrum_str = "256.0153:100.000000" # mz:intensity格式 example_total_mass = 255.0136185 # 总精确质量 # 生成SELFIES result = generate_selfies( formula=example_formula, spectrum_str=example_spectrum_str, total_mass=example_total_mass, max_length=512, temperature=0.7, top_p=0.95 ) print("生成的SELFIES字符串：") print(result)修改代码，解决问题Some weights of LlamaForCausalLM were not initialized from the model checkpoint at ./llama3.2-SELFIES and are newly initialized: ['lm_head.weight', 'model.embed_tokens.weight', 'model.layers.0.input_layernorm.weight', 'model.layers.0.mlp.down_proj.weight', 'model.layers.0.mlp.gate_proj.weight', 'model.layers.0.mlp.up_proj.weight', 'model.layers.0.post_attention_layernorm.weight', 'model.layers.0.self_attn.k_proj.weight', 'model.layers.0.self_attn.o_proj.weight', 'model.layers.0.self_attn.q_proj.weight', 'model.layers.0.self_attn.v_proj.weight', 'model.layers.1.input_layernorm.weight', 'model.layers.1.mlp.down_proj.weight', 'model.layers.1.mlp.gate_proj.weight', 'model.layers.1.mlp.up_proj.weight', 'model.layers.1.post_attention_layernorm.weight', 'model.layers.1.self_attn.k_proj.weight', 'model.layers.1.self_attn.o_proj.weight', 'model.layers.1.self_attn.q_proj.weight', 'model.layers.1.self_attn.v_proj.weight', 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'model.layers.7.input_layernorm.weight', 'model.layers.7.mlp.down_proj.weight', 'model.layers.7.mlp.gate_proj.weight', 'model.layers.7.mlp.up_proj.weight', 'model.layers.7.post_attention_layernorm.weight', 'model.layers.7.self_attn.k_proj.weight', 'model.layers.7.self_attn.o_proj.weight', 'model.layers.7.self_attn.q_proj.weight', 'model.layers.7.self_attn.v_proj.weight', 'model.layers.8.input_layernorm.weight', 'model.layers.8.mlp.down_proj.weight', 'model.layers.8.mlp.gate_proj.weight', 'model.layers.8.mlp.up_proj.weight', 'model.layers.8.post_attention_layernorm.weight', 'model.layers.8.self_attn.k_proj.weight', 'model.layers.8.self_attn.o_proj.weight', 'model.layers.8.self_attn.q_proj.weight', 'model.layers.8.self_attn.v_proj.weight', 'model.layers.9.input_layernorm.weight', 'model.layers.9.mlp.down_proj.weight', 'model.layers.9.mlp.gate_proj.weight', 'model.layers.9.mlp.up_proj.weight', 'model.layers.9.post_attention_layernorm.weight', 'model.layers.9.self_attn.k_proj.weight', 'model.layers.9.self_attn.o_proj.weight', 'model.layers.9.self_attn.q_proj.weight', 'model.layers.9.self_attn.v_proj.weight', 'model.norm.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

model = LlamaWithEncoder(config, base_model=None, ...) # 注意，这里不传入base_model，而是在下面更新权重 # 获取自定义模型当前的状态字典 custom_state_dict = model.state_dict() # 将new_state_dict中属于...

这段transformer的代码有什么用：#位置编码 class TransformerEmbedding(nn.Module): def __init__(self, config): super().__init__() # hyper params self.vocab_size = config["vocab_size"] self.hidden_size = config["d_model"] # 词向量维度 self.pad_idx = config["pad_idx"] dropout_rate = config["dropout"] self.max_length = config["max_length"] # layers,设置padding_idx可以让pad的词向量全为0 self.word_embedding = nn.Embedding( self.vocab_size, self.hidden_size, padding_idx=self.pad_idx ) self.pos_embedding = nn.Embedding( self.max_length, self.hidden_size, _weight=self.get_positional_encoding( self.max_length, self.hidden_size ),# 位置编码，权重通过get_positional_encoding函数计算得到 ) self.pos_embedding.weight.requires_grad_(False) # 不更新位置编码的权重 self.dropout = nn.Dropout(dropout_rate) # 随机失活层 def get_word_embedding_weights(self): return self.word_embedding.weight # 计算位置信息 @classmethod def get_positional_encoding(self, max_length, hidden_size):#max_length是最大长度，hidden_size是embedding维度相等 # Compute the positional encodings once in log space. pe = torch.zeros(max_length, hidden_size) # 初始化位置编码 # .unsqueeze(1) 是将这个一维张量转换为二维张量，即将其形状从 (max_length,) 变为 (max_length, 1)。这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。 position = torch.arange(0, max_length).unsqueeze(1) # 位置信息,从0到max_length-1 div_term = torch.exp( torch.arange(0, hidden_size, 2) * -(torch.log(torch.Tensor([10000.0])) / hidden_size) )# 计算位置编码的权重,为了性能考量（是数学上的对数函数分解） pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) return pe def forward(self, input_ids): # input_ids: [batch_size, seq_len] seq_len = input_ids.shape[1] assert ( seq_len <= self.max_length ), f"input sequence length should no more than {self.max_length} but got {seq_len}" position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) print(position_ids) #为了调试 # embedding word_embeds = self.word_embedding(input_ids) # 词嵌入 pos_embeds = self.pos_embedding(position_ids) # 位置编码 embeds = word_embeds + pos_embeds embeds = self.dropout(embeds) return embeds def plot_position_embedding(position_embedding):# 绘制位置编码 plt.pcolormesh(position_embedding) # 绘制位置编码矩阵 plt.xlabel('Depth') plt.ylabel('Position') plt.colorbar() # 颜色条，-1到1的颜色范围 plt.show() position_embedding = TransformerEmbedding.get_positional_encoding(64, 128) plot_position_embedding(position_embedding)

初始化函数__init__中接收config参数，里面应该包含一些超参数，比如vocab_size、d_model（即hidden_size）、pad_idx、dropout和max_length。然后，代码中定义了两个嵌入层：word_embedding和pos_embedding，以及一...

# Copyright 2018 DeepMind Technologies Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Code defining LEO inner loop. See "Meta-Learning with Latent Embedding Optimization" by Rusu et al. (https://arxiv.org/pdf/1807.05960.pdf). """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import range from six.moves import zip import sonnet as snt import tensorflow as tf import tensorflow_probability as tfp import data as data_module def get_orthogonality_regularizer(orthogonality_penalty_weight): """Returns the orthogonality regularizer.""" def orthogonality(weight): """Calculates the layer-wise penalty encouraging orthogonality.""" with tf.name_scope(None, "orthogonality", [weight]) as name: w2 = tf.matmul(weight, weight, transpose_b=True) wn = tf.norm(weight, ord=2, axis=1, keepdims=True) + 1e-32 correlation_matrix = w2 / tf.matmul(wn, wn, transpose_b=True) matrix_size = correlation_matrix.get_shape().as_list()[0] base_dtype = weight.dtype.base_dtype identity = tf.eye(matrix_size, dtype=base_dtype) weight_corr = tf.reduce_mean( tf.squared_difference(correlation_matrix, identity)) return tf.multiply( tf.cast(orthogonality_penalty_weight, base_dtype), weight_corr, name=name) return orthogonality class LEO(snt.AbstractModule): """Sonnet module implementing the inner loop of LEO.""" def __init__(self, config=None, use_64bits_dtype=True, name="leo"): super(LEO, self).__init__(name=name) self._float_dtype = tf.float64 if use_64bits_dtype else tf.float32 self._int_dtype = tf.int64 if use_64bits_dtype else tf.int32 self._inner_unroll_length = config["inner_unroll_length"] self._finetuning_unroll_length = config["finetuning_unroll_length"] self._inner_lr_init = config["inner_lr_init"] self._finetuning_lr_init = config["finetuning_lr_init"] self._num_latents = config["num_latents"] self._dropout_rate = config["dropout_rate"] self._kl_weight = config["kl_weight"] # beta self._encoder_penalty_weight = config["encoder_penalty_weight"] # gamma self._l2_penalty_weight = config["l2_penalty_weight"] # lambda_1 # lambda_2 self._orthogonality_penalty_weight = config["orthogonality_penalty_weight"] assert self._inner_unroll_length > 0, ("Positive unroll length is necessary" " to create the graph") def _build(self, data, is_meta_training=True): """Connects the LEO module to the graph, creating the variables. Args: data: A data_module.ProblemInstance constaining Tensors with the following shapes: - tr_input: (N, K, dim) - tr_output: (N, K, 1) - tr_info: (N, K) - val_input: (N, K_valid, dim) - val_output: (N, K_valid, 1) - val_info: (N, K_valid) where N is the number of classes (as in N-way) and K and the and K_valid are numbers of training and validation examples within a problem instance correspondingly (as in K-shot), and dim is the dimensionality of the embedding. is_meta_training: A boolean describing whether we run in the training mode. Returns: Tensor with the inner validation loss of LEO (include both adaptation in the latent space and finetuning). """ if isinstance(data, list): data = data_module.ProblemInstance(*data) self.is_meta_training = is_meta_training self.save_problem_instance_stats(data.tr_input) latents, kl = self.forward_encoder(data) tr_loss, adapted_classifier_weights, encoder_penalty = self.leo_inner_loop( data, latents) val_loss, val_accuracy = self.finetuning_inner_loop( data, tr_loss, adapted_classifier_weights) val_loss += self._kl_weight * kl val_loss += self._encoder_penalty_weight * encoder_penalty # The l2 regularization is is already added to the graph when constructing # the snt.Linear modules. We pass the orthogonality regularizer separately, # because it is not used in self.grads_and_vars. regularization_penalty = ( self._l2_regularization + self._decoder_orthogonality_reg) batch_val_loss = tf.reduce_mean(val_loss) batch_val_accuracy = tf.reduce_mean(val_accuracy) return batch_val_loss + regularization_penalty, batch_val_accuracy @snt.reuse_variables def leo_inner_loop(self, data, latents): with tf.variable_scope("leo_inner"): inner_lr = tf.get_variable( "lr", [1, 1, self._num_latents], dtype=self._float_dtype, initializer=tf.constant_initializer(self._inner_lr_init)) starting_latents = latents loss, _ = self.forward_decoder(data, latents) for _ in range(self._inner_unroll_length): loss_grad = tf.gradients(loss, latents) # dLtrain/dz latents -= inner_lr * loss_grad[0] loss, classifier_weights = self.forward_decoder(data, latents) if self.is_meta_training: encoder_penalty = tf.losses.mean_squared_error( labels=tf.stop_gradient(latents), predictions=starting_latents) encoder_penalty = tf.cast(encoder_penalty, self._float_dtype) else: encoder_penalty = tf.constant(0., self._float_dtype) return loss, classifier_weights, encoder_penalty @snt.reuse_variables def finetuning_inner_loop(self, data, leo_loss, classifier_weights): tr_loss = leo_loss with tf.variable_scope("finetuning"): finetuning_lr = tf.get_variable( "lr", [1, 1, self.embedding_dim], dtype=self._float_dtype, initializer=tf.constant_initializer(self._finetuning_lr_init)) for _ in range(self._finetuning_unroll_length): loss_grad = tf.gradients(tr_loss, classifier_weights) classifier_weights -= finetuning_lr * loss_grad[0] tr_loss, _ = self.calculate_inner_loss(data.tr_input, data.tr_output, classifier_weights) val_loss, val_accuracy = self.calculate_inner_loss( data.val_input, data.val_output, classifier_weights) return val_loss, val_accuracy @snt.reuse_variables def forward_encoder(self, data): encoder_outputs = self.encoder(data.tr_input) relation_network_outputs = self.relation_network(encoder_outputs) latent_dist_params = self.average_codes_per_class(relation_network_outputs) latents, kl = self.possibly_sample(latent_dist_params) return latents, kl @snt.reuse_variables def forward_decoder(self, data, latents): weights_dist_params = self.decoder(latents) # Default to glorot_initialization and not stddev=1. fan_in = self.embedding_dim.value fan_out = self.num_classes.value stddev_offset = np.sqrt(2. / (fan_out + fan_in)) classifier_weights, _ = self.possibly_sample(weights_dist_params, stddev_offset=stddev_offset) tr_loss, _ = self.calculate_inner_loss(data.tr_input, data.tr_output, classifier_weights) return tr_loss, classifier_weights @snt.reuse_variables def encoder(self, inputs): with tf.variable_scope("encoder"): after_dropout = tf.nn.dropout(inputs, rate=self.dropout_rate) regularizer = tf.contrib.layers.l2_regularizer(self._l2_penalty_weight) initializer = tf.initializers.glorot_uniform(dtype=self._float_dtype) encoder_module = snt.Linear( self._num_latents, use_bias=False, regularizers={"w": regularizer}, initializers={"w": initializer}, ) outputs = snt.BatchApply(encoder_module)(after_dropout) return outputs @snt.reuse_variables def relation_network(self, inputs): with tf.variable_scope("relation_network"): regularizer = tf.contrib.layers.l2_regularizer(self._l2_penalty_weight) initializer = tf.initializers.glorot_uniform(dtype=self._float_dtype) relation_network_module = snt.nets.MLP( [2 * self._num_latents] * 3, use_bias=False, regularizers={"w": regularizer}, initializers={"w": initializer}, ) total_num_examples = self.num_examples_per_class*self.num_classes inputs = tf.reshape(inputs, [total_num_examples, self._num_latents]) left = tf.tile(tf.expand_dims(inputs, 1), [1, total_num_examples, 1]) right = tf.tile(tf.expand_dims(inputs, 0), [total_num_examples, 1, 1]) concat_codes = tf.concat([left, right], axis=-1) outputs = snt.BatchApply(relation_network_module)(concat_codes) outputs = tf.reduce_mean(outputs, axis=1) # 2 * latents, because we are returning means and variances of a Gaussian outputs = tf.reshape(outputs, [self.num_classes, self.num_examples_per_class, 2 * self._num_latents]) return outputs @snt.reuse_variables def decoder(self, inputs): with tf.variable_scope("decoder"): l2_regularizer = tf.contrib.layers.l2_regularizer(self._l2_penalty_weight) orthogonality_reg = get_orthogonality_regularizer( self._orthogonality_penalty_weight) initializer = tf.initializers.glorot_uniform(dtype=self._float_dtype) # 2 * embedding_dim, because we are returning means and variances decoder_module = snt.Linear( 2 * self.embedding_dim, use_bias=False, regularizers={"w": l2_regularizer}, initializers={"w": initializer}, ) outputs = snt.BatchApply(decoder_module)(inputs) self._orthogonality_reg = orthogonality_reg(decoder_module.w) return outputs def average_codes_per_class(self, codes): codes = tf.reduce_mean(codes, axis=1, keep_dims=True) # K dimension # Keep the shape (N, K, *) codes = tf.tile(codes, [1, self.num_examples_per_class, 1]) return codes def possibly_sample(self, distribution_params, stddev_offset=0.): means, unnormalized_stddev = tf.split(distribution_params, 2, axis=-1) stddev = tf.exp(unnormalized_stddev) stddev -= (1. - stddev_offset) stddev = tf.maximum(stddev, 1e-10) distribution = tfp.distributions.Normal(loc=means, scale=stddev) if not self.is_meta_training: return means, tf.constant(0., dtype=self._float_dtype) samples = distribution.sample() kl_divergence = self.kl_divergence(samples, distribution) return samples, kl_divergence def kl_divergence(self, samples, normal_distribution): random_prior = tfp.distributions.Normal( loc=tf.zeros_like(samples), scale=tf.ones_like(samples)) kl = tf.reduce_mean( normal_distribution.log_prob(samples) - random_prior.log_prob(samples)) return kl def predict(self, inputs, weights): after_dropout = tf.nn.dropout(inputs, rate=self.dropout_rate) # This is 3-dimensional equivalent of a matrix product, where we sum over # the last (embedding_dim) dimension. We get [N, K, N, K] tensor as output. per_image_predictions = tf.einsum("ijk,lmk->ijlm", after_dropout, weights) # Predictions have shape [N, K, N]: for each image ([N, K] of them), what # is the probability of a given class (N)? predictions = tf.reduce_mean(per_image_predictions, axis=-1) return predictions def calculate_inner_loss(self, inputs, true_outputs, classifier_weights): model_outputs = self.predict(inputs, classifier_weights) model_predictions = tf.argmax( model_outputs, -1, output_type=self._int_dtype) accuracy = tf.contrib.metrics.accuracy(model_predictions, tf.squeeze(true_outputs, axis=-1)) return self.loss_fn(model_outputs, true_outputs), accuracy def save_problem_instance_stats(self, instance): num_classes, num_examples_per_class, embedding_dim = instance.get_shape() if hasattr(self, "num_classes"): assert self.num_classes == num_classes, ( "Given different number of classes (N in N-way) in consecutive runs.") if hasattr(self, "num_examples_per_class"): assert self.num_examples_per_class == num_examples_per_class, ( "Given different number of examples (K in K-shot) in consecutive" "runs.") if hasattr(self, "embedding_dim"): assert self.embedding_dim == embedding_dim, ( "Given different embedding dimension in consecutive runs.") self.num_classes = num_classes self.num_examples_per_class = num_examples_per_class self.embedding_dim = embedding_dim @property def dropout_rate(self): return self._dropout_rate if self.is_meta_training else 0.0 def loss_fn(self, model_outputs, original_classes): original_classes = tf.squeeze(original_classes, axis=-1) # Tensorflow doesn't handle second order gradients of a sparse_softmax yet. one_hot_outputs = tf.one_hot(original_classes, depth=self.num_classes) return tf.nn.softmax_cross_entropy_with_logits_v2( labels=one_hot_outputs, logits=model_outputs) def grads_and_vars(self, metatrain_loss): """Computes gradients of metatrain_loss, avoiding NaN. Uses a fixed penalty of 1e-4 to enforce only the l2 regularization (and not minimize the loss) when metatrain_loss or any of its gradients with respect to trainable_vars are NaN. In practice, this approach pulls the variables back into a feasible region of the space when the loss or its gradients are not defined. Args: metatrain_loss: A tensor with the LEO meta-training loss. Returns: A tuple with: metatrain_gradients: A list of gradient tensors. metatrain_variables: A list of variables for this LEO model. """ metatrain_variables = self.trainable_variables metatrain_gradients = tf.gradients(metatrain_loss, metatrain_variables) nan_loss_or_grad = tf.logical_or( tf.is_nan(metatrain_loss), tf.reduce_any([tf.reduce_any(tf.is_nan(g)) for g in metatrain_gradients])) regularization_penalty = ( 1e-4 / self._l2_penalty_weight * self._l2_regularization) zero_or_regularization_gradients = [ g if g is not None else tf.zeros_like(v) for v, g in zip(tf.gradients(regularization_penalty, metatrain_variables), metatrain_variables)] metatrain_gradients = tf.cond(nan_loss_or_grad, lambda: zero_or_regularization_gradients, lambda: metatrain_gradients, strict=True) return metatrain_gradients, metatrain_variables @property def _l2_regularization(self): return tf.cast( tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), dtype=self._float_dtype) @property def _decoder_orthogonality_reg(self): return self._orthogonality_reg 中解码器将潜在代码映射为分类器参数，然后将分类器参数用于更新分类器，再通过更新后的分类器求损失，这些对应哪段代码？

def __init__(self, latent_dim, hidden_dim, output_shape): super().__init__() self.fc1 = nn.Linear(latent_dim, hidden_dim) self.fc2 = nn.Linear(hidden_dim, np.prod(output_shape)) # 输出展平的参数...

# 这是一个示例 Python 脚本。 # 按 Shift+F10 执行或将其替换为您的代码。 # 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 import argparse import math import pickle import torch import torch.nn as nn import torch.nn.functional as F from tqdm import tqdm from omegaconf import OmegaConf from sklearn.metrics import f1_score from torch.utils.data import Dataset, DataLoader from torch.nn import TransformerEncoderLayer, TransformerEncoder restypes = [ 'A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V' ] unsure_restype = 'X' unknown_restype = 'U' def make_dataset(data_config, train_rate=0.7, valid_rate=0.2): data_path = data_config.data_path with open(data_path, 'rb') as f: data = pickle.load(f) total_number = len(data) train_sep = int(total_number * train_rate) valid_sep = int(total_number * (train_rate + valid_rate)) train_data_dicts = data[:train_sep] valid_data_dicts = data[train_sep:valid_sep] test_data_dicts = data[valid_sep:] train_dataset = DisProtDataset(train_data_dicts) valid_dataset = DisProtDataset(valid_data_dicts) test_dataset = DisProtDataset(test_data_dicts) return train_dataset, valid_dataset, test_dataset class DisProtDataset(Dataset): def __init__(self, dict_data): sequences = [d['sequence'] for d in dict_data] labels = [d['label'] for d in dict_data] assert len(sequences) == len(labels) self.sequences = sequences self.labels = labels self.residue_mapping = {'X':20} self.residue_mapping.update(dict(zip(restypes, range(len(restypes))))) def __len__(self): return len(self.sequences) def __getitem__(self, idx): sequence = torch.zeros(len(self.sequences[idx]), len(self.residue_mapping)) for i, c in enumerate(self.sequences[idx]): if c not in restypes: c = 'X' sequence[i][self.residue_mapping[c]] = 1 label = torch.tensor([int(c) for c in self.labels[idx]], dtype=torch.long) return sequence, label class PositionalEncoding(nn.Module): def __init__(self, d_model, dropout=0.0, max_len=40): super().__init__() position = torch.arange(max_len).unsqueeze(1) div_term = torch.exp( torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) ) pe = torch.zeros(1, max_len, d_model) pe[0, :, 0::2] = torch.sin(position * div_term) pe[0, :, 1::2] = torch.cos(position * div_term) self.register_buffer("pe", pe) self.dropout = nn.Dropout(p=dropout) def forward(self, x): if len(x.shape) == 3: x = x + self.pe[:, : x.size(1)] elif len(x.shape) == 4: x = x + self.pe[:, :x.size(1), None, :] return self.dropout(x) class DisProtModel(nn.Module): def __init__(self, model_config): super().__init__() self.d_model = model_config.d_model self.n_head = model_config.n_head self.n_layer = model_config.n_layer self.input_layer = nn.Linear(model_config.i_dim, self.d_model) self.position_embed = PositionalEncoding(self.d_model, max_len=20000) self.input_norm = nn.LayerNorm(self.d_model) self.dropout_in = nn.Dropout(p=0.1) encoder_layer = TransformerEncoderLayer( d_model=self.d_model, nhead=self.n_head, activation='gelu', batch_first=True) self.transformer = TransformerEncoder(encoder_layer, num_layers=self.n_layer) self.output_layer = nn.Sequential( nn.Linear(self.d_model, self.d_model), nn.GELU(), nn.Dropout(p=0.1), nn.Linear(self.d_model, model_config.o_dim) ) def forward(self, x): x = self.input_layer(x) x = self.position_embed(x) x = self.input_norm(x) x = self.dropout_in(x) x = self.transformer(x) x = self.output_layer(x) return x def metric_fn(pred, gt): pred = pred.detach().cpu() gt = gt.detach().cpu() pred_labels = torch.argmax(pred, dim=-1).view(-1) gt_labels = gt.view(-1) score = f1_score(y_true=gt_labels, y_pred=pred_labels, average='micro') return score if __name__ == '__main__': device = 'cuda' if torch.cuda.is_available() else 'cpu' parser = argparse.ArgumentParser('IDRs prediction') parser.add_argument('--config_path', default='./config.yaml') args = parser.parse_args() config = OmegaConf.load(args.config_path) train_dataset, valid_dataset, test_dataset = make_dataset(config.data) train_dataloader = DataLoader(dataset=train_dataset, **config.train.dataloader) valid_dataloader = DataLoader(dataset=valid_dataset, batch_size=1, shuffle=False) model = DisProtModel(config.model) model = model.to(device) optimizer = torch.optim.AdamW(model.parameters(), lr=config.train.optimizer.lr, weight_decay=config.train.optimizer.weight_decay) loss_fn = nn.CrossEntropyLoss() model.eval() metric = 0. with torch.no_grad(): for sequence, label in valid_dataloader: sequence = sequence.to(device) label = label.to(device) pred = model(sequence) metric += metric_fn(pred, label) print("init f1_score:", metric / len(valid_dataloader)) for epoch in range(config.train.epochs): # train loop progress_bar = tqdm( train_dataloader, initial=0, desc=f"epoch:{epoch:03d}", ) model.train() total_loss = 0. for sequence, label in progress_bar: sequence = sequence.to(device) label = label.to(device) pred = model(sequence) loss = loss_fn(pred.permute(0, 2, 1), label) progress_bar.set_postfix(loss=loss.item()) total_loss += loss.item() optimizer.zero_grad() loss.backward() optimizer.step() avg_loss = total_loss / len(train_dataloader) # valid loop model.eval() metric = 0. with torch.no_grad(): for sequence, label in valid_dataloader: sequence = sequence.to(device) label = label.to(device) pred = model(sequence) metric += metric_fn(pred, label) print(f"avg_training_loss: {avg_loss}, f1_score: {metric / len(valid_dataloader)}") # 保存当前 epoch 的模型 save_path = f"model.pkl" torch.save(model.state_dict(), save_path) print(f"Model saved to {save_path}") 帮我分析一下这个代码是干什么的

def __init__(self, d_model, dropout=0.1, max_len=5000): super().__init__() self.dropout = nn.Dropout(p=dropout) position = torch.arange(max_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, ...

import tensorflow as tf import numpy as np from keras import datasets import cv2 (train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data() import matplotlib.pyplot as plt class PositionEmbedding(tf.keras.layers.Layer): """位置编码层，为序列添加位置信息""" def __init__(self, max_len, embed_dim): super().__init__() self.max_len = max_len self.embed_dim = embed_dim # 可学习的位置编码矩阵：max_len × embed_dim self.pos_emb = tf.keras.layers.Embedding( input_dim=max_len, output_dim=embed_dim ) def call(self, x): # 生成位置索引序列（0到序列长度-1） positions = tf.range(start=0, limit=tf.shape(x)[1], delta=1) # 将位置编码加到输入特征上 return x + self.pos_emb(positions) # 添加get_config方法 def get_config(self): config = super().get_config() # 获取父类配置 config.update({ "max_len": self.max_len, "embed_dim": self.embed_dim }) return config class TransformerBlock(tf.keras.layers.Layer): """Transformer编码块，包含多头注意力和前馈网络""" def __init__(self, embed_dim, num_heads): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads # 多头注意力机制（核心组件） self.att = tf.keras.layers.MultiHeadAttention( num_heads=num_heads, key_dim=embed_dim # 每个头的维度 ) # 前馈网络（两全连接层） self.ffn = tf.keras.Sequential([ tf.keras.layers.Dense(embed_dim*4, activation="relu"), # 扩展维度 tf.keras.layers.Dense(embed_dim) # 恢复维度 ]) # 层归一化组件 self.layernorm1 = tf.keras.layers.LayerNormalization() self.layernorm2 = tf.keras.layers.LayerNormalization() def call(self, inputs): # 自注意力机制（q=k=v=inputs） attn_output = self.att(inputs, inputs) # 残差连接 + 层归一化 out1 = self.layernorm1(inputs + attn_output) # 前馈网络处理 ffn_output = self.ffn(out1) # 再次残差连接 + 层归一化 return self.layernorm2(out1 + ffn_output) # 添加get_config方法 def get_config(self): config = super().get_config() # 获取父类配置 config.update({ "embed_dim": self.embed_dim, "num_heads": self.num_heads, }) return config model = tf.keras.models.load_model('transform_model.keras', custom_objects={ "PositionEmbedding": PositionEmbedding, "TransformerBlock": TransformerBlock } ) # Evaluate the restored model loss, acc = model.evaluate(test_images, test_labels, verbose=2) pre = model.predict(test_images) # 对所有测试图片进行预测 print('Restored model, accuracy: {:5.2f}%'.format(100 * acc)) # 输出第一张图片的预测结果 print( pre[1]*100) print( np.argmax(pre[3])) plt.imshow(test_images[3]) plt.show() cv2.waitKey(0)PS D:\source\test3> & C:/ProgramData/anaconda3/envs/LSTM-TESFLOW/python.exe d:/source/test3/predict.py 2025-03-11 16:01:35.284449: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2025-03-11 16:01:35.291695: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Traceback (most recent call last): File "d:/source/test3/predict.py", line 75, in <module> model = tf.keras.models.load_model('transform_model.keras', File "C:\ProgramData\anaconda3\envs\LSTM-TESFLOW\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\ProgramData\anaconda3\envs\LSTM-TESFLOW\lib\site-packages\keras\engine\base_layer.py", line 783, in from_config return cls(**config) TypeError: __init__() got an unexpected keyword argument 'name'

def __init__(self, embed_dim, num_heads, **kwargs): # 增加**kwargs super().__init__(**kwargs) # 传递kwargs给父类 self.embed_dim = embed_dim self.num_heads = num_heads self.att = tf.keras.layers....

RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. !pip install transformers datasets torch rouge-score matplotlib import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader from transformers import BertTokenizerFast import time import numpy as np from datasets import load_dataset from rouge_score import rouge_scorer import matplotlib.pyplot as plt from IPython.display import clear_output # 设备配置 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # 数据预处理（严格过滤无效样本） class SummaryDataset(Dataset): def __init__(self, dataset_split, tokenizer, max_article_len=384, max_summary_len=96, subset_size=0.01): self.tokenizer = tokenizer self.max_article_len = max_article_len self.max_summary_len = max_summary_len self.subset = dataset_split.select(range(int(len(dataset_split) * subset_size))) # 严格过滤无效样本 self.articles = [] self.summaries = [] self.vocab = set(tokenizer.vocab.keys()) for item in self.subset: article = item['article'].strip() summary = item['highlights'].strip() if len(article) > 20 and len(summary) > 10: article_tokens = tokenizer.tokenize(article) summary_tokens = tokenizer.tokenize(summary) if all(t in self.vocab for t in article_tokens) and all(t in self.vocab for t in summary_tokens): self.articles.append(article) self.summaries.append(summary) self.pad_token_id = tokenizer.pad_token_id self.unk_token_id = tokenizer.unk_token_id def __len__(self): return len(self.articles) def __getitem__(self, idx): src = self.tokenizer( self.articles[idx], max_length=self.max_article_len, truncation=True, padding='max_length', return_tensors='pt', add_special_tokens=True ) tgt = self.tokenizer( self.summaries[idx], max_length=self.max_summary_len, truncation=True, padding='max_length', return_tensors='pt', add_special_tokens=True ) tgt_labels = tgt['input_ids'].squeeze() tgt_labels[tgt_labels == self.pad_token_id] = -100 # 忽略填充 tgt_labels[tgt_labels >= len(self.tokenizer.vocab)] = self.unk_token_id # 过滤无效id return { 'input_ids': src['input_ids'].squeeze(), 'attention_mask': src['attention_mask'].squeeze(), 'labels': tgt_labels } # 基础Seq2Seq模型 class BasicEncoder(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0) self.gru = nn.GRU(emb_dim, hidden_dim, num_layers=2, batch_first=True, bidirectional=True) self.fc_hidden = nn.Linear(hidden_dim * 2, hidden_dim) def forward(self, src): embedded = self.embedding(src) outputs, hidden = self.gru(embedded) # 取第二层双向隐藏状态 forward_hidden = hidden[-2, :, :] # 第二层正向 backward_hidden = hidden[-1, :, :] # 第二层反向 hidden = torch.cat([forward_hidden, backward_hidden], dim=1) # (batch, 2*hidden_dim) hidden = self.fc_hidden(hidden).unsqueeze(0) # (1, batch, hidden_dim) return hidden class BasicDecoder(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0) self.gru = nn.GRU(emb_dim + hidden_dim, hidden_dim, num_layers=1, batch_first=True) self.fc = nn.Linear(hidden_dim * 2 + emb_dim, vocab_size) def forward(self, input_ids, hidden, context): input_embedded = self.embedding(input_ids.unsqueeze(1)) # (batch, 1, emb_dim) input_combined = torch.cat([input_embedded, context.unsqueeze(1)], dim=2) # (batch, 1, emb_dim+hidden_dim) output, hidden = self.gru(input_combined, hidden) # (batch, 1, hidden_dim) output = output.squeeze(1) # (batch, hidden_dim) combined = torch.cat([output, context, input_embedded.squeeze(1)], dim=1) # (batch, 2*hidden_dim+emb_dim) logits = self.fc(combined) return logits, hidden class BasicSeq2Seq(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.encoder = BasicEncoder(vocab_size, emb_dim, hidden_dim) self.decoder = BasicDecoder(vocab_size, emb_dim, hidden_dim) self.device = device self.sos_token_id = 101 # [CLS] self.eos_token_id = 102 # [SEP] self.unk_token_id = 100 # [UNK] def forward(self, src, tgt): hidden = self.encoder(src) context = hidden.squeeze(0) batch_size, tgt_len = tgt.size() outputs = torch.zeros(batch_size, tgt_len, self.decoder.fc.out_features).to(device) input_ids = tgt[:, 0] for t in range(1, tgt_len): logits, hidden = self.decoder(input_ids, hidden, context) outputs[:, t] = logits input_ids = tgt[:, t] return outputs def generate(self, src, max_length=80): src = src.to(device) hidden = self.encoder(src) context = hidden.squeeze(0) # 修正后的生成初始化 generated = torch.full((src.size(0), 1), self.sos_token_id, device=device) # 注意这里的修正 for _ in range(max_length-1): logits, hidden = self.decoder(generated[:, -1], hidden, context) next_token = torch.argmax(logits, dim=1, keepdim=True) # 防止过早生成标点 if generated.size(1) < 5: punctuation = [',', '.', ';', ':', '!', '?', "'", '"', '', '~'] punct_ids = [self.tokenizer.convert_tokens_to_ids(p) for p in punctuation] if next_token.item() in punct_ids: # 替换为最常见的实词 next_token = torch.tensor([[self.tokenizer.convert_tokens_to_ids('the')]], device=device) generated = torch.cat([generated, next_token], dim=1) if (next_token == self.eos_token_id).all(): break return generated # 注意力Seq2Seq模型 class Attention(nn.Module): def __init__(self, hidden_dim): super().__init__() self.W = nn.Linear(2 * hidden_dim, hidden_dim) self.v = nn.Linear(hidden_dim, 1, bias=False) def forward(self, hidden, encoder_outputs): src_len = encoder_outputs.size(1) hidden = hidden.unsqueeze(1).repeat(1, src_len, 1) # (batch, src_len, hidden_dim) combined = torch.cat([hidden, encoder_outputs], dim=2) # (batch, src_len, 2*hidden_dim) energy = self.v(torch.tanh(self.W(combined))).squeeze(2) # (batch, src_len) return torch.softmax(energy, dim=1) class AttnEncoder(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0) self.lstm = nn.LSTM(emb_dim, hidden_dim, num_layers=2, batch_first=True, bidirectional=True, dropout=0.1) self.fc_hidden = nn.Linear(hidden_dim * 2, hidden_dim) # 双向输出拼接 self.fc_cell = nn.Linear(hidden_dim * 2, hidden_dim) def forward(self, src): embedded = self.embedding(src) outputs, (hidden, cell) = self.lstm(embedded) # outputs: (batch, src_len, 2*hidden_dim) # 取第二层双向隐藏状态 hidden = torch.cat([hidden[-2, :, :], hidden[-1, :, :]], dim=1) # (batch, 2*hidden_dim) cell = torch.cat([cell[-2, :, :], cell[-1, :, :]], dim=1) hidden = self.fc_hidden(hidden).unsqueeze(0) # (1, batch, hidden_dim) cell = self.fc_cell(cell).unsqueeze(0) return outputs, (hidden, cell) class AttnDecoder(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0) self.attention = Attention(hidden_dim) self.lstm = nn.LSTM(emb_dim + 2 * hidden_dim, hidden_dim, num_layers=1, batch_first=True) self.fc = nn.Linear(hidden_dim + emb_dim, vocab_size) def forward(self, input_ids, hidden, cell, encoder_outputs): input_embedded = self.embedding(input_ids.unsqueeze(1)) # (batch, 1, emb_dim) attn_weights = self.attention(hidden.squeeze(0), encoder_outputs) # (batch, src_len) context = torch.bmm(attn_weights.unsqueeze(1), encoder_outputs) # (batch, 1, 2*hidden_dim) lstm_input = torch.cat([input_embedded, context], dim=2) # (batch, 1, emb_dim+2*hidden_dim) output, (hidden, cell) = self.lstm(lstm_input, (hidden, cell)) # output: (batch, 1, hidden_dim) logits = self.fc(torch.cat([output.squeeze(1), input_embedded.squeeze(1)], dim=1)) # (batch, vocab_size) return logits, hidden, cell class AttnSeq2Seq(nn.Module): def __init__(self, vocab_size, emb_dim=128, hidden_dim=256): super().__init__() self.encoder = AttnEncoder(vocab_size, emb_dim, hidden_dim) self.decoder = AttnDecoder(vocab_size, emb_dim, hidden_dim) self.device = device self.sos_token_id = 101 # [CLS] self.eos_token_id = 102 # [SEP] self.unk_token_id = 100 # [UNK] def forward(self, src, tgt): encoder_outputs, (hidden, cell) = self.encoder(src) batch_size, tgt_len = tgt.size() outputs = torch.zeros(batch_size, tgt_len, self.decoder.fc.out_features).to(device) input_ids = tgt[:, 0] for t in range(1, tgt_len): logits, hidden, cell = self.decoder(input_ids, hidden, cell, encoder_outputs) outputs[:, t] = logits input_ids = tgt[:, t] return outputs def generate(self, src, max_length=80): encoder_outputs, (hidden, cell) = self.encoder(src) # 修正后的生成初始化 generated = torch.full((src.size(0), 1), self.sos_token_id, device=device) # 注意这里的修正 for _ in range(max_length-1): logits, hidden, cell = self.decoder(generated[:, -1], hidden, cell, encoder_outputs) next_token = torch.argmax(logits, dim=1, keepdim=True) # 防止过早生成标点 if generated.size(1) < 5: punctuation = [',', '.', ';', ':', '!', '?', "'", '"', '', '~'] punct_ids = [self.tokenizer.convert_tokens_to_ids(p) for p in punctuation] if next_token.item() in punct_ids: # 替换为最常见的实词 next_token = torch.tensor([[self.tokenizer.convert_tokens_to_ids('the')]], device=device) generated = torch.cat([generated, next_token], dim=1) if (next_token == self.eos_token_id).all(): break return generated # Transformer模型 class PositionalEncoding(nn.Module): def __init__(self, d_model, max_len=5000): super().__init__() pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) self.register_buffer('pe', pe.unsqueeze(0)) def forward(self, x): return x + self.pe[:, :x.size(1)] class TransformerModel(nn.Module): def __init__(self, vocab_size, d_model=128, nhead=8, num_layers=3, dim_feedforward=512, max_len=5000): super().__init__() self.embedding = nn.Embedding(vocab_size, d_model, padding_idx=0) self.pos_encoder = PositionalEncoding(d_model, max_len) # 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评估函数 def evaluate_model(model, val_loader, tokenizer, num_examples=2): model.eval() scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True) rouge_scores = {'rouge1': [], 'rouge2': [], 'rougeL': []} valid_count = 0 with torch.no_grad(): for i, batch in enumerate(val_loader): src = batch['input_ids'].to(device) tgt = batch['labels'].to(device) generated = model.generate(src) for s, p, t in zip(src, generated, tgt): src_txt = tokenizer.decode(s, skip_special_tokens=True) pred_txt = tokenizer.decode(p, skip_special_tokens=True) true_txt = tokenizer.decode(t[t != -100], skip_special_tokens=True) if len(pred_txt.split()) > 3 and len(true_txt.split()) > 3: valid_count += 1 if valid_count <= num_examples: print(f"\n原文: {src_txt[:100]}...") print(f"生成: {pred_txt}") print(f"参考: {true_txt[:80]}...") print("-"*60) if true_txt and pred_txt: scores = scorer.score(true_txt, pred_txt) for key in rouge_scores: rouge_scores[key].append(scores[key].fmeasure) if valid_count > 0: avg_scores = {key: sum(rouge_scores[key])/len(rouge_scores[key]) for key in rouge_scores} print(f"\n评估结果 (基于{valid_count}个样本):") print(f"ROUGE-1: {avg_scores['rouge1']*100:.2f}%") print(f"ROUGE-2: {avg_scores['rouge2']*100:.2f}%") print(f"ROUGE-L: {avg_scores['rougeL']*100:.2f}%") else: print("警告：未生成有效摘要") avg_scores = {key: 0.0 for key in rouge_scores} return avg_scores # 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生成摘要 inputs = tokenizer( input_text, max_length=384, truncation=True, padding='max_length', return_tensors='pt' ).to(device) print("\n生成摘要中...") all_summaries = [] for i, model in enumerate(models): model.eval() with torch.no_grad(): generated = model.generate(inputs["input_ids"]) summary = tokenizer.decode(generated[0], skip_special_tokens=True) all_summaries.append(summary) # 打印结果 print(f"\n{model_names[i]} 摘要:") print("-"*50) print(summary) print("-"*50) print("\n所有模型摘要对比:") for i, (name, summary) in enumerate(zip(model_names, all_summaries)): print(f"{i+1}. {name}: {summary}") # 主程序 print("加载数据集...") dataset = load_dataset("cnn_dailymail", "3.0.0") tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') vocab_size = len(tokenizer.vocab) # 准备训练数据 print("准备训练数据...") train_ds = SummaryDataset(dataset['train'], tokenizer, subset_size=0.01) # 使用1%的数据 val_ds = SummaryDataset(dataset['validation'], tokenizer, subset_size=0.01) train_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=0) val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=0) # 定义损失函数 criterion = nn.CrossEntropyLoss(ignore_index=-100) # 训练基础Seq2Seq print("\n" + "="*60) print("训练基础Seq2Seq模型") print("="*60) basic_model = BasicSeq2Seq(vocab_size).to(device) trained_basic, basic_time = train_model(basic_model, train_loader, None, criterion, epochs=3) basic_rouge = evaluate_model(trained_basic, val_loader, tokenizer) # 训练注意力Seq2Seq print("\n" + "="*60) print("训练注意力Seq2Seq模型") print("="*60) attn_model = AttnSeq2Seq(vocab_size).to(device) trained_attn, attn_time = train_model(attn_model, train_loader, None, criterion, epochs=3) attn_rouge = evaluate_model(trained_attn, val_loader, tokenizer) # 训练Transformer print("\n" + "="*60) print("训练Transformer模型") print("="*60) transformer_model = TransformerModel(vocab_size).to(device) trained_transformer, transformer_time = train_model(transformer_model, train_loader, None, criterion, epochs=3) transformer_rouge = evaluate_model(trained_transformer, val_loader, tokenizer) # 可视化模型性能 print("\n" + "="*60) print("模型性能对比") print("="*60) model_names = ['基础Seq2Seq', '注意力Seq2Seq', 'Transformer'] train_times = [basic_time/60, attn_time/60, transformer_time/60] rouge_scores = [basic_rouge, attn_rouge, transformer_rouge] visualize_model_performance(model_names, train_times, rouge_scores) # 交互式测试 print("\n" + "="*60) print("交互式文本摘要测试") print("="*60) print("提示：输入一段文本，将同时生成三个模型的摘要结果") interactive_summarization( [trained_basic, trained_attn, trained_transformer], tokenizer, model_names ) 修改完错误后发完整代码给我

self).__init__()self.bert=BertForMaskedLM.from_pretrained('bert-base-uncased')#添加自定义摘要输出层self.summary_head=nn.Linear(self.bert.config.hidden_size,1)defforward(self,input_ids,attention_mask):...
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