import os import math import logging import torch import torch.nn as nn from transformers.configuration_albert import AlbertConfig logger = logging.getLogger(__name__) def load_tf_weights_in_albert(model, config, tf_checkpoint_path): """ Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions.") raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info("Converting TensorFlow checkpoint from {}".format(tf_path)) # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info("Loading TF weight {} with shape {}".format(name, shape)) array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) print(model) for name, array in zip(names, arrays): og = name name = name.replace("transformer/group_0/inner_group_0", "transformer") name = name.replace("LayerNorm", "layer_norm") name = name.replace("ffn_1", "ffn") name = name.replace("ffn/intermediate/output", "ffn_output") name = name.replace("attention_1", "attention") name = name.replace("cls/predictions/transform", "predictions") name = name.replace("transformer/layer_norm_1", "transformer/attention/output/LayerNorm") name = name.split('/') print(name) pointer = model for m_name in name: if re.fullmatch(r'[A-Za-z]+_\d+', m_name): l = re.split(r'_(\d+)', m_name) else: l = [m_name] if l[0] == 'kernel' or l[0] == 'gamma': pointer = getattr(pointer, 'weight') elif l[0] == 'output_bias' or l[0] == 'beta': pointer = getattr(pointer, 'bias') elif l[0] == 'output_weights': pointer = getattr(pointer, 'weight') elif l[0] == 'squad': pointer = getattr(pointer, 'classifier') else: try: pointer = getattr(pointer, l[0]) except AttributeError: logger.info("Skipping {}".format("/".join(name))) continue if len(l) >= 2: num = int(l[1]) pointer = pointer[num] if m_name[-11:] == '_embeddings': pointer = getattr(pointer, 'weight') elif m_name == 'kernel': array = np.transpose(array) print("transposed") try: assert pointer.shape == array.shape except AssertionError as e: e.args += (pointer.shape, array.shape) raise print("Initialize PyTorch weight {} from {}".format(name, og)) pointer.data = torch.from_numpy(array) return model class AlbertEmbeddings(nn.Module): """ Construct the embeddings from word, position and token_type embeddings. """ def __init__(self, config): super(AlbertEmbeddings, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=0) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size) self.layer_norm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids=None, position_ids=None): seq_length = input_ids.size(1) if position_ids is None: position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) word_embeddings = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = word_embeddings + position_embeddings + token_type_embeddings embeddings = self.layer_norm(embeddings) embeddings = self.dropout(embeddings) return embeddings def get_word_embeddings_table(self): return self.word_embeddings class AlbertModel(nn.Module): def __init__(self, config): super(AlbertModel, self).__init__() self.config = config self.embeddings = AlbertEmbeddings(config) self.encoder = AlbertEncoder(config) self.pooler = nn.Linear(config.hidden_size, config.hidden_size) self.pooler_activation = nn.Tanh() def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): if attention_mask is None: attention_mask = torch.ones_like(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 if head_mask is not None: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility else: head_mask = [None] * self.config.num_hidden_layers embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids) encoder_outputs = self.encoder(embedding_output, extended_attention_mask, head_mask=head_mask) sequence_output = encoder_outputs[0] print(sequence_output.shape, sequence_output[:, 0].shape, self.pooler(sequence_output[:, 0]).shape) pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] # add hidden_states and attentions if they are here return outputs class AlbertForMaskedLM(nn.Module): def __init__(self, config): super(AlbertForMaskedLM, self).__init__() self.config = config self.bert = AlbertModel(config) self.layer_norm = nn.LayerNorm(config.embedding_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.dense = nn.Linear(config.hidden_size, config.embedding_size) self.word_embeddings = nn.Linear(config.embedding_size, config.vocab_size) def tie_weights(self): """ Make sure we are sharing the input and output embeddings. Export to TorchScript can't handle parameter sharing so we are cloning them instead. """ self._tie_or_clone_weights(self.classifier.word_embeddings, self.transformer.embeddings.word_embeddings) def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): hidden_states = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)[0] hidden_states = self.dense(hidden_states) hidden_states = gelu_new(hidden_states) hidden_states = self.layer_norm(hidden_states) logits = self.word_embeddings(hidden_states) return logits class AlbertAttention(nn.Module): def __init__(self, config): super(AlbertAttention, self).__init__() if config.hidden_size % config.num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, config.num_attention_heads)) self.output_attentions = config.output_attentions self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, input_ids, attention_mask=None, head_mask=None): mixed_query_layer = self.query(input_ids) mixed_key_layer = self.key(input_ids) mixed_value_layer = self.value(input_ids) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) reshaped_context_layer = context_layer.view(*new_context_layer_shape) w = self.dense.weight.T.view(16, 64, 1024) b = self.dense.bias projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b projected_context_layer = self.dropout(projected_context_layer) layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer) return layernormed_context_layer, projected_context_layer, reshaped_context_layer, context_layer, attention_scores, attention_probs, attention_mask class AlbertTransformer(nn.Module): def __init__(self, config): super(AlbertTransformer, self).__init__() self.config =config self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attention = AlbertAttention(config) self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states, attention_mask=None, head_mask=None): for i in range(self.config.num_hidden_layers): attention_output = self.attention(hidden_states, attention_mask)[0] ffn_output = self.ffn(attention_output) ffn_output = gelu_new(ffn_output) ffn_output = self.ffn_output(ffn_output) hidden_states = self.layer_norm(ffn_output + attention_output) return hidden_states def gelu_new(x): """ Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT). Also see https://arxiv.org/abs/1606.08415 """ return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) class AlbertEncoder(nn.Module): def __init__(self, config): super(AlbertEncoder, self).__init__() self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size) self.transformer = AlbertTransformer(config) def forward(self, hidden_states, attention_mask=None, head_mask=None): hidden_states = self.embedding_hidden_mapping_in(hidden_states) hidden_states = self.transformer(hidden_states, attention_mask, head_mask) outputs = (hidden_states,) if self.output_hidden_states: outputs = outputs + (all_hidden_states,) if self.output_attentions: outputs = outputs + (all_attentions,) return outputs # last-layer hidden state, (all hidden states), (all attentions) # config = AlbertConfig.from_json_file("config.json") # # model = AlbertForMaskedLM(config) # model = AlbertModel(config) # model = load_tf_weights_in_albert(model, config, "albert/albert") # print(model) # input_ids = torch.tensor([[31, 51, 99], [15, 5, 0]]) # input_mask = torch.tensor([[1, 1, 1], [1, 1, 0]]) # segment_ids = torch.tensor([[0, 0, 1], [0, 0, 0]]) # # sequence_output, pooled_outputs = model() # logits = model(input_ids, attention_mask=input_mask, token_type_ids=segment_ids)[1] # embeddings_output = # print("pooled output", logits) # # print("Pooled output", pooled_outputs) config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config.json") model = AlbertModel(config) model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert/albert")