一個簡單的主入口是這樣滴：
import sys sys.path.append('..') import torch from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM # Load pre-trained model tokenizer (vocabulary) tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Tokenized input text = "Who was Jim Henson ? Jim Henson was a puppeteer" tokenized_text = tokenizer.tokenize(text) # Mask a token that we will try to predict back with `BertForMaskedLM` masked_index = 6 tokenized_text[masked_index] = '[MASK]' assert tokenized_text == ['who', 'was', 'jim', 'henson', '?', 'jim', '[MASK]', 'was', 'a', 'puppet', '##eer'] # Convert token to vocabulary indices indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text) # Define sentence A and B indices associated to 1st and 2nd sentences (see paper) segments_ids = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] # segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # Convert inputs to PyTorch tensors tokens_tensor = torch.tensor([indexed_tokens]).to('cuda') segments_tensors = torch.tensor([segments_ids]).to('cuda') # ========================= BertForMaskedLM ============================== # Load pre-trained model (weights) model = BertForMaskedLM.from_pretrained('bert-base-uncased') model.to('cuda') model.eval()
入口就是倒數第三行。
然后進到這里這個from_pretrained方法，這里的代碼邏輯還是是有順序的：
@classmethod def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None, *inputs, **kwargs): """ Instantiate a PreTrainedBertModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if needed. Params: pretrained_model_name: either: - a str with the name of a pre-trained model to load selected in the list of: . `bert-base-uncased` . `bert-large-uncased` . `bert-base-cased` . `bert-large-cased` . `bert-base-multilingual-uncased` . `bert-base-multilingual-cased` . `bert-base-chinese` - a path or url to a pretrained model archive containing: . `bert_config.json` a configuration file for the model . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance cache_dir: an optional path to a folder in which the pre-trained models will be cached. state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models *inputs, **kwargs: additional input for the specific Bert class (ex: num_labels for BertForSequenceClassification) """ if pretrained_model_name in PRETRAINED_MODEL_ARCHIVE_MAP: archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name] else: archive_file = pretrained_model_name # redirect to the cache, if necessary try: resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir) except FileNotFoundError: logger.error( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url but couldn't find any file " "associated to this path or url.".format( pretrained_model_name, ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()), archive_file)) return None if resolved_archive_file == archive_file: logger.info("loading archive file {}".format(archive_file)) else: logger.info("loading archive file {} from cache at {}".format( archive_file, resolved_archive_file)) tempdir = None if os.path.isdir(resolved_archive_file): serialization_dir = resolved_archive_file else: # Extract archive to temp dir tempdir = tempfile.mkdtemp() logger.info("extracting archive file {} to temp dir {}".format( resolved_archive_file, tempdir)) with tarfile.open(resolved_archive_file, 'r:gz') as archive: archive.extractall(tempdir) serialization_dir = tempdir # Load config config_file = os.path.join(serialization_dir, CONFIG_NAME) config = BertConfig.from_json_file(config_file) logger.info("Model config {}".format(config)) # Instantiate model. model = cls(config, *inputs, **kwargs) if state_dict is None: weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) state_dict = torch.load(weights_path) old_keys = [] new_keys = [] for key in state_dict.keys(): new_key = None if 'gamma' in key: new_key = key.replace('gamma', 'weight') if 'beta' in key: new_key = key.replace('beta', 'bias') if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): state_dict[new_key] = state_dict.pop(old_key) missing_keys = [] unexpected_keys = [] error_msgs = [] # copy state_dict so _load_from_state_dict can modify it metadata = getattr(state_dict, '_metadata', None) state_dict = state_dict.copy() if metadata is not None: state_dict._metadata = metadata def load(module, prefix=''): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + '.') load(model, prefix='' if hasattr(model, 'bert') else 'bert.') #todo: 從這邊，model.cls.predictions.bias,這個偏值項的權值被從全0替換 if len(missing_keys) > 0: logger.info("Weights of {} not initialized from pretrained model: {}".format( model.__class__.__name__, missing_keys)) if len(unexpected_keys) > 0: logger.info("Weights from pretrained model not used in {}: {}".format( model.__class__.__name__, unexpected_keys)) if tempdir: # Clean up temp dir shutil.rmtree(tempdir) return model
方法雖然長一點，但功能只是簡單的載入模型然后load所有的預訓練參數
然后注意其中這個load方法：
def load(module, prefix=''): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + '.') load(model, prefix='' if hasattr(model, 'bert') else 'bert.') #todo: 從這邊，model.cls.predictions.bias,這個偏值項的權值被從全0替換
這個load方法載入了所有的預訓練參數，那么這個bias到底是指的哪一個bias呢，是這個類：
class BertLMPredictionHead(nn.Module): """ Arch: - BertPredictionHeadTransform (Input=torch.Size([1, 11, 768]), Output=torch.Size([1, 11, 768])) - Dense (768, 768) - Activation (gelu) - LayerNorm - Linear (768, 30522) y = W * x + b y = self.decoder.weight * self.decoder + self.bias i.e., y = torch.Size([30522, 768]) * torch.Size([768, 30522]) + torch.Size([30522]) Input: torch.Size([1, 11, 768]) Output: torch.Size([1, 11, 30522]) The purpose is to Decode. """ def __init__(self, config, bert_model_embedding_weights): super(BertLMPredictionHead, self).__init__() self.transform = BertPredictionHeadTransform(config) """ bert_model_embedding_weights.size(): torch.Size([30522, 768]) """ # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(bert_model_embedding_weights.size(1), bert_model_embedding_weights.size(0), bias=False) # torch.Size([768, 30522]) self.decoder.weight = bert_model_embedding_weights # torch.Size([30522, 768]) self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0))) # torch.Size([30522]) def forward(self, hidden_states): """ hidden_states: torch.Size([1, 11, 768]) torch.Size([1, 11, 768]) --> torch.Size([1, 11, 768]) """ hidden_states = self.transform(hidden_states) """ To predict the corresponding word in vocab. Each of 11 positions has a tensor size=[30522] same to the size of vocab. """ hidden_states = self.decoder(hidden_states) + self.bias # torch.Size([1, 11, 30522]) return hidden_states
就是這個bias：
self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0))) # torch.Size([30522])
但是為啥我覺得奇怪呢，因為這個類並不是bert的model本身的權值，而是一個擴展類，用來預測【musk】的ids的，然后找到了這個預訓練權重的大state_dict，是這樣的：
'cls.predictions.bias' = {Tensor: 30522} tensor([-0.4191, -0.4202, -0.4191, ..., -0.7900, -0.7822, -0.4965]) 'cls.predictions.transform.dense.weight' = {Tensor: 768} tensor([[ 0.3681, 0.0147, 0.0430, ..., 0.0384, -0.0296, 0.0227],\n [ 0.0034, 0.2647, -0.0618, ..., -0.0397, -0.0335, 0.0203],\n [ 0.0179, -0.0060, 0.1788, ..., 0.0267, 0.0555, -0.0432],\n ...,\n [ 0.0784, 0.0172, 0.0583, ..., 0.3548, 0.0209, -0.0261],\n [ 0.0175, -0.0466, 0.0834, ..., 0.0069, 0.2132, -0.0503],\n [-0.0832, 0.0461, 0.0490, ..., -0.0116, -0.0594, 0.3525]]) 'cls.predictions.transform.dense.bias' = {Tensor: 768} tensor([ 5.3890e-02, 1.0068e-01, 4.5532e-02, 2.7030e-02, 3.8845e-02,\n 3.3157e-02, 4.1188e-02, 2.8206e-02, 2.4197e-02, 1.3879e-01,\n 4.4386e-02, 4.8806e-02, 3.4415e-02, 5.9976e-02, 4.2772e-02,\n 2.5261e-02, 1.0533e-01, 4.1858e-02, 4.9016e-02, 9.8930e-02,\n 2.4026e-02, 4.1394e-02, 4.2273e-02, 2.9724e-02, 1.0857e-01,\n 4.8379e-02, 3.6337e-02, 5.2781e-02, 2.9902e-02, 2.6919e-02,\n 2.1127e-02, 4.8463e-02, 5.7389e-02, 4.8581e-02, 9.8151e-02,\n 6.3899e-02, 4.4544e-02, 4.9595e-02, 4.5315e-02, 3.5128e-02,\n 3.4962e-02, 6.9260e-02, 4.8273e-02, 4.3921e-02, 3.6126e-02,\n 3.9017e-02, 4.7681e-02, 4.1840e-02, 4.2173e-02, 5.2243e-02,\n 3.3530e-02, 4.3681e-02, 9.2896e-02, -1.3240e-01, 3.5652e-02,\n 3.2232e-02, 6.1398e-02, 3.9744e-02, 4.3546e-02, 3.7697e-02,\n 3.2834e-02, 2.5923e-02, -7.8080e-02, 2.7405e-02, 7.5468e-02,\n 3.8439e-02, 8.4586e-02, 3.0094e-02, 3.6... 'cls.predictions.decoder.weight' = {Tensor: 30522} tensor([[-0.0102, -0.0615, -0.0265, ..., -0.0199, -0.0372, -0.0098],\n [-0.0117, -0.0600, -0.0323, ..., -0.0168, -0.0401, -0.0107],\n [-0.0198, -0.0627, -0.0326, ..., -0.0165, -0.0420, -0.0032],\n ...,\n [-0.0218, -0.0556, -0.0135, ..., -0.0043, -0.0151, -0.0249],\n [-0.0462, -0.0565, -0.0019, ..., 0.0157, -0.0139, -0.0095],\n [ 0.0015, -0.0821, -0.0160, ..., -0.0081, -0.0475, 0.0753]]) 'cls.seq_relationship.weight' = {Tensor: 2} tensor([[-0.0154, -0.0062, -0.0137, ..., -0.0128, -0.0099, 0.0006],\n [ 0.0058, 0.0120, 0.0128, ..., 0.0088, 0.0137, -0.0162]]) 'cls.seq_relationship.bias' = {Tensor: 2} tensor([ 0.0211, -0.0021])
一共一百多個不同名稱的權值，其中有這么幾個權值命名是cls開頭的
然后這個看了下代碼邏輯，是按照名稱載入的，所以這個模型的cls.predictions.bias就被替換掉了，本來是全0的。
我很奇怪，因為我覺得這個dict里面不太應該有這么個東西，后來想了一下，預訓練的時候也可能用到了這個musk的功能類，權值就被保存下來了，
同時，cls.predictions.decoder.weight這個，也好像被重置了，那么它這個模型一開始就把這個weight用Embedding層的weight初始化，是沒必要的，可以從代碼里發現，這個權值從bert里直接塞過去是這樣的：
Parameter containing: tensor([[-0.0102, -0.0615, -0.0265, ..., -0.0199, -0.0372, -0.0098], [-0.0117, -0.0600, -0.0323, ..., -0.0168, -0.0401, -0.0107], [-0.0198, -0.0627, -0.0326, ..., -0.0165, -0.0420, -0.0032], ..., [-0.0218, -0.0556, -0.0135, ..., -0.0043, -0.0151, -0.0249], [-0.0462, -0.0565, -0.0019, ..., 0.0157, -0.0139, -0.0095], [ 0.0015, -0.0821, -0.0160, ..., -0.0081, -0.0475, 0.0753]], requires_grad=True)
-0.0102, -0.0615。。。。這個數字和上面第四行那個開頭是一致的，可以簡單斷言這倆權值是相同的。
也就是Embedding層里面的權重，
至於結論嘛、。。。。這個預訓練權重可以再縮縮。。（弱弱的手動狗頭）