# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # 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. """ TF 2.0 Transformer XL model. """ import logging import tensorflow as tf from .configuration_transfo_xl import TransfoXLConfig from .file_utils import add_start_docstrings, add_start_docstrings_to_callable from .modeling_tf_transfo_xl_utilities import TFAdaptiveSoftmaxMask from .modeling_tf_utils import TFPreTrainedModel, get_initializer, keras_serializable, shape_list from .tokenization_utils import BatchEncoding logger = logging.getLogger(__name__) TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP = { "transfo-xl-wt103": "https://cdn.huggingface.co/transfo-xl-wt103-tf_model.h5", } class TFPositionalEmbedding(tf.keras.layers.Layer): def __init__(self, demb, **kwargs): super().__init__(**kwargs) self.inv_freq = 1 / (10000 ** (tf.range(0, demb, 2.0) / demb)) def call(self, pos_seq, bsz=None): sinusoid_inp = tf.einsum("i,j->ij", pos_seq, self.inv_freq) pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], -1) if bsz is not None: return tf.tile(pos_emb[:, None, :], [1, bsz, 1]) else: return pos_emb[:, None, :] class TFPositionwiseFF(tf.keras.layers.Layer): def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5, init_std=0.02, **kwargs): super().__init__(**kwargs) self.d_model = d_model self.d_inner = d_inner self.dropout = dropout self.layer_1 = tf.keras.layers.Dense( d_inner, kernel_initializer=get_initializer(init_std), activation=tf.nn.relu, name="CoreNet_._0" ) self.drop_1 = tf.keras.layers.Dropout(dropout) self.layer_2 = tf.keras.layers.Dense(d_model, kernel_initializer=get_initializer(init_std), name="CoreNet_._3") self.drop_2 = tf.keras.layers.Dropout(dropout) self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layer_norm") self.pre_lnorm = pre_lnorm def call(self, inp, training=False): if self.pre_lnorm: # layer normalization + positionwise feed-forward core_out = self.layer_norm(inp) core_out = self.layer_1(core_out) core_out = self.drop_1(core_out, training=training) core_out = self.layer_2(core_out) core_out = self.drop_2(core_out, training=training) # residual connection output = core_out + inp else: # positionwise feed-forward core_out = self.layer_1(inp) core_out = self.drop_1(core_out, training=training) core_out = self.layer_2(core_out) core_out = self.drop_2(core_out, training=training) # residual connection + layer normalization output = self.layer_norm(inp + core_out) return output class TFRelPartialLearnableMultiHeadAttn(tf.keras.layers.Layer): def __init__( self, n_head, d_model, d_head, dropout, dropatt=0, tgt_len=None, ext_len=None, mem_len=None, pre_lnorm=False, r_r_bias=None, r_w_bias=None, output_attentions=False, layer_norm_epsilon=1e-5, init_std=0.02, **kwargs ): super().__init__(**kwargs) self.output_attentions = output_attentions self.n_head = n_head self.d_model = d_model self.d_head = d_head self.dropout = dropout self.qkv_net = tf.keras.layers.Dense( 3 * n_head * d_head, kernel_initializer=get_initializer(init_std), use_bias=False, name="qkv_net" ) self.drop = tf.keras.layers.Dropout(dropout) self.dropatt = tf.keras.layers.Dropout(dropatt) self.o_net = tf.keras.layers.Dense( d_model, kernel_initializer=get_initializer(init_std), use_bias=False, name="o_net" ) self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layer_norm") self.scale = 1 / (d_head ** 0.5) self.pre_lnorm = pre_lnorm if r_r_bias is not None and r_w_bias is not None: # Biases are shared self.r_r_bias = r_r_bias self.r_w_bias = r_w_bias else: self.r_r_bias = None self.r_w_bias = None self.r_net = tf.keras.layers.Dense( self.n_head * self.d_head, kernel_initializer=get_initializer(init_std), use_bias=False, name="r_net" ) def build(self, input_shape): if self.r_r_bias is None or self.r_w_bias is None: # Biases are not shared self.r_r_bias = self.add_weight( shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_r_bias" ) self.r_w_bias = self.add_weight( shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_w_bias" ) super().build(input_shape) def _rel_shift(self, x): x_size = shape_list(x) x = tf.pad(x, [[0, 0], [1, 0], [0, 0], [0, 0]]) x = tf.reshape(x, [x_size[1] + 1, x_size[0], x_size[2], x_size[3]]) x = tf.slice(x, [1, 0, 0, 0], [-1, -1, -1, -1]) x = tf.reshape(x, x_size) return x def call(self, inputs, training=False): w, r, attn_mask, mems, head_mask = inputs qlen, rlen, bsz = shape_list(w)[0], shape_list(r)[0], shape_list(w)[1] if mems is not None: cat = tf.concat([mems, w], 0) if self.pre_lnorm: w_heads = self.qkv_net(self.layer_norm(cat)) else: w_heads = self.qkv_net(cat) r_head_k = self.r_net(r) w_head_q, w_head_k, w_head_v = tf.split(w_heads, 3, axis=-1) w_head_q = w_head_q[-qlen:] else: if self.pre_lnorm: w_heads = self.qkv_net(self.layer_norm(w)) else: w_heads = self.qkv_net(w) r_head_k = self.r_net(r) w_head_q, w_head_k, w_head_v = tf.split(w_heads, 3, axis=-1) klen = shape_list(w_head_k)[0] w_head_q = tf.reshape(w_head_q, (qlen, bsz, self.n_head, self.d_head)) # qlen x bsz x n_head x d_head w_head_k = tf.reshape(w_head_k, (klen, bsz, self.n_head, self.d_head)) # qlen x bsz x n_head x d_head w_head_v = tf.reshape(w_head_v, (klen, bsz, self.n_head, self.d_head)) # qlen x bsz x n_head x d_head r_head_k = tf.reshape(r_head_k, (rlen, self.n_head, self.d_head)) # qlen x n_head x d_head # compute attention score rw_head_q = w_head_q + self.r_w_bias # qlen x bsz x n_head x d_head AC = tf.einsum("ibnd,jbnd->ijbn", rw_head_q, w_head_k) # qlen x klen x bsz x n_head rr_head_q = w_head_q + self.r_r_bias BD = tf.einsum("ibnd,jnd->ijbn", rr_head_q, r_head_k) # qlen x klen x bsz x n_head BD = self._rel_shift(BD) # [qlen x klen x bsz x n_head] attn_score = AC + BD attn_score = attn_score * self.scale # compute attention probability if attn_mask is not None: attn_mask_t = attn_mask[:, :, None, None] attn_score = attn_score * (1 - attn_mask_t) - 1e30 * attn_mask_t # [qlen x klen x bsz x n_head] attn_prob = tf.nn.softmax(attn_score, axis=1) attn_prob = self.dropatt(attn_prob, training=training) # Mask heads if we want to if head_mask is not None: attn_prob = attn_prob * head_mask # compute attention vector attn_vec = tf.einsum("ijbn,jbnd->ibnd", attn_prob, w_head_v) # [qlen x bsz x n_head x d_head] attn_vec_sizes = shape_list(attn_vec) attn_vec = tf.reshape(attn_vec, (attn_vec_sizes[0], attn_vec_sizes[1], self.n_head * self.d_head)) # linear projection attn_out = self.o_net(attn_vec) attn_out = self.drop(attn_out, training=training) if self.pre_lnorm: # residual connection outputs = [w + attn_out] else: # residual connection + layer normalization outputs = [self.layer_norm(w + attn_out)] if self.output_attentions: outputs.append(attn_prob) return outputs class TFRelPartialLearnableDecoderLayer(tf.keras.layers.Layer): def __init__( self, n_head, d_model, d_head, d_inner, dropout, tgt_len=None, ext_len=None, mem_len=None, dropatt=0.0, pre_lnorm=False, r_w_bias=None, r_r_bias=None, output_attentions=False, layer_norm_epsilon=1e-5, init_std=0.02, **kwargs ): super().__init__(**kwargs) self.dec_attn = TFRelPartialLearnableMultiHeadAttn( n_head, d_model, d_head, dropout, tgt_len=tgt_len, ext_len=ext_len, mem_len=mem_len, dropatt=dropatt, pre_lnorm=pre_lnorm, r_w_bias=r_w_bias, r_r_bias=r_r_bias, init_std=init_std, output_attentions=output_attentions, layer_norm_epsilon=layer_norm_epsilon, name="dec_attn", ) self.pos_ff = TFPositionwiseFF( d_model, d_inner, dropout, pre_lnorm=pre_lnorm, init_std=init_std, layer_norm_epsilon=layer_norm_epsilon, name="pos_ff", ) def call(self, inputs, training=False): dec_inp, r, dec_attn_mask, mems, head_mask = inputs attn_outputs = self.dec_attn([dec_inp, r, dec_attn_mask, mems, head_mask], training=training) ff_output = self.pos_ff(attn_outputs[0], training=training) outputs = [ff_output] + attn_outputs[1:] return outputs class TFAdaptiveEmbedding(tf.keras.layers.Layer): def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, init_std=0.02, sample_softmax=False, **kwargs): super().__init__(**kwargs) self.n_token = n_token self.d_embed = d_embed self.init_std = init_std self.cutoffs = cutoffs + [n_token] self.div_val = div_val self.d_proj = d_proj self.emb_scale = d_proj ** 0.5 self.cutoff_ends = [0] + self.cutoffs self.emb_layers = [] self.emb_projs = [] if div_val == 1: raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint else: for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] d_emb_i = d_embed // (div_val ** i) self.emb_layers.append( tf.keras.layers.Embedding( r_idx - l_idx, d_emb_i, embeddings_initializer=get_initializer(init_std), name="emb_layers_._{}".format(i), ) ) def build(self, input_shape): for i in range(len(self.cutoffs)): d_emb_i = self.d_embed // (self.div_val ** i) self.emb_projs.append( self.add_weight( shape=(d_emb_i, self.d_proj), initializer=get_initializer(self.init_std), trainable=True, name="emb_projs_._{}".format(i), ) ) super().build(input_shape) def call(self, inp): if self.div_val == 1: raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint else: inp_flat = tf.reshape(inp, (-1,)) emb_flat = tf.zeros([shape_list(inp_flat)[0], self.d_proj]) for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] mask_i = (inp_flat >= l_idx) & (inp_flat < r_idx) inp_i = tf.boolean_mask(inp_flat, mask_i) - l_idx emb_i = self.emb_layers[i](inp_i) emb_i = tf.einsum("id,de->ie", emb_i, self.emb_projs[i]) mask_idx = tf.cast(tf.where(mask_i), dtype=tf.int64) emb_flat += tf.scatter_nd(mask_idx, emb_i, tf.cast(shape_list(emb_flat), dtype=tf.int64)) embed_shape = shape_list(inp) + [self.d_proj] embed = tf.reshape(emb_flat, embed_shape) embed *= self.emb_scale return embed @keras_serializable class TFTransfoXLMainLayer(tf.keras.layers.Layer): config_class = TransfoXLConfig def __init__(self, config, **kwargs): super().__init__(**kwargs) self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.n_token = config.vocab_size self.d_embed = config.d_embed self.d_model = config.d_model self.n_head = config.n_head self.d_head = config.d_head self.untie_r = config.untie_r self.word_emb = TFAdaptiveEmbedding( config.vocab_size, config.d_embed, config.d_model, config.cutoffs, div_val=config.div_val, init_std=config.init_std, name="word_emb", ) self.drop = tf.keras.layers.Dropout(config.dropout) self.n_layer = config.n_layer self.tgt_len = config.tgt_len self.mem_len = config.mem_len self.ext_len = config.ext_len self.max_klen = config.tgt_len + config.ext_len + config.mem_len self.attn_type = config.attn_type self.layers = [] if config.attn_type == 0: # the default attention for i in range(config.n_layer): self.layers.append( TFRelPartialLearnableDecoderLayer( config.n_head, config.d_model, config.d_head, config.d_inner, config.dropout, tgt_len=config.tgt_len, ext_len=config.ext_len, mem_len=config.mem_len, dropatt=config.dropatt, pre_lnorm=config.pre_lnorm, r_w_bias=None if self.untie_r else self.r_w_bias, r_r_bias=None if self.untie_r else self.r_r_bias, output_attentions=self.output_attentions, layer_norm_epsilon=config.layer_norm_epsilon, init_std=config.init_std, name="layers_._{}".format(i), ) ) else: # learnable embeddings and absolute embeddings raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint self.same_length = config.same_length self.clamp_len = config.clamp_len if self.attn_type == 0: # default attention self.pos_emb = TFPositionalEmbedding(self.d_model, name="pos_emb") else: # learnable embeddings and absolute embeddings raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint def build(self, input_shape): if not self.untie_r: self.r_w_bias = self.add_weight( shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_w_bias" ) self.r_r_bias = self.add_weight( shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_r_bias" ) super().build(input_shape) def get_input_embeddings(self): return self.word_emb def _resize_token_embeddings(self, new_num_tokens): return self.word_emb def backward_compatible(self): self.sample_softmax = -1 def reset_length(self, tgt_len, ext_len, mem_len): self.tgt_len = tgt_len self.mem_len = mem_len self.ext_len = ext_len def _prune_heads(self, heads): raise NotImplementedError def init_mems(self, bsz): if self.mem_len > 0: mems = [] for i in range(self.n_layer): empty = tf.zeros([self.mem_len, bsz, self.d_model]) mems.append(empty) return mems else: return None def _update_mems(self, hids, mems, mlen, qlen): # does not deal with None if mems is None: return None # mems is not None assert len(hids) == len(mems), "len(hids) != len(mems)" # There are `mlen + qlen` steps that can be cached into mems # For the next step, the last `ext_len` of the `qlen` tokens # will be used as the extended context. Hence, we only cache # the tokens from `mlen + qlen - self.ext_len - self.mem_len` # to `mlen + qlen - self.ext_len`. new_mems = [] end_idx = mlen + max(0, qlen - 0 - self.ext_len) beg_idx = max(0, end_idx - self.mem_len) for i in range(len(hids)): cat = tf.concat([mems[i], hids[i]], axis=0) tf.stop_gradient(cat) new_mems.append(cat[beg_idx:end_idx]) return new_mems def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, training=False): if isinstance(inputs, (tuple, list)): input_ids = inputs[0] mems = inputs[1] if len(inputs) > 1 else mems head_mask = inputs[2] if len(inputs) > 2 else head_mask inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds assert len(inputs) <= 4, "Too many inputs." elif isinstance(inputs, (dict, BatchEncoding)): input_ids = inputs.get("input_ids") mems = inputs.get("mems", mems) head_mask = inputs.get("head_mask", head_mask) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) assert len(inputs) <= 4, "Too many inputs." else: input_ids = inputs # the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library # so we transpose here from shape [bsz, len] to shape [len, bsz] if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_ids = tf.transpose(input_ids, perm=(1, 0)) qlen, bsz = shape_list(input_ids) elif inputs_embeds is not None: inputs_embeds = tf.transpose(inputs_embeds, perm=(1, 0, 2)) qlen, bsz = shape_list(inputs_embeds)[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if mems is None: mems = self.init_mems(bsz) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer) # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.n_layer if inputs_embeds is not None: word_emb = inputs_embeds else: word_emb = self.word_emb(input_ids) mlen = shape_list(mems[0])[0] if mems is not None else 0 klen = mlen + qlen attn_mask = tf.ones([qlen, qlen]) mask_u = tf.linalg.band_part(attn_mask, 0, -1) mask_dia = tf.linalg.band_part(attn_mask, 0, 0) attn_mask_pad = tf.zeros([qlen, mlen]) dec_attn_mask = tf.concat([attn_mask_pad, mask_u - mask_dia], 1) if self.same_length: mask_l = tf.linalg.band_part(attn_mask, -1, 0) dec_attn_mask = tf.concat([dec_attn_mask[:, :qlen] + mask_l - mask_dia, dec_attn_mask[:, qlen:]], 1) # ::: PyTorch masking code for reference ::: # if self.same_length: # all_ones = word_emb.new_ones((qlen, klen), dtype=torch.uint8) # mask_len = klen - self.mem_len # if mask_len > 0: # mask_shift_len = qlen - mask_len # else: # mask_shift_len = qlen # dec_attn_mask = (torch.triu(all_ones, 1+mlen) # + torch.tril(all_ones, -mask_shift_len))[:, :, None] # -1 # else: # dec_attn_mask = torch.triu( # word_emb.new_ones((qlen, klen), dtype=torch.uint8), diagonal=1+mlen)[:,:,None] hids = [] attentions = [] if self.attn_type == 0: # default pos_seq = tf.range(klen - 1, -1, -1.0) if self.clamp_len > 0: pos_seq = tf.minimum(pos_seq, self.clamp_len) pos_emb = self.pos_emb(pos_seq) core_out = self.drop(word_emb, training=training) pos_emb = self.drop(pos_emb, training=training) for i, layer in enumerate(self.layers): hids.append(core_out) mems_i = None if mems is None else mems[i] layer_outputs = layer([core_out, pos_emb, dec_attn_mask, mems_i, head_mask[i]], training=training) core_out = layer_outputs[0] if self.output_attentions: attentions.append(layer_outputs[1]) else: # learnable embeddings and absolute embeddings raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint core_out = self.drop(core_out, training=training) new_mems = self._update_mems(hids, mems, mlen, qlen) # We transpose back here to shape [bsz, len, hidden_dim] outputs = [tf.transpose(core_out, perm=(1, 0, 2)), new_mems] if self.output_hidden_states: # Add last layer and transpose to library standard shape [bsz, len, hidden_dim] hids.append(core_out) hids = list(tf.transpose(t, perm=(1, 0, 2)) for t in hids) outputs.append(hids) if self.output_attentions: # Transpose to library standard shape [bsz, n_heads, query_seq_len, key_seq_len] attentions = list(tf.transpose(t, perm=(2, 3, 0, 1)) for t in attentions) outputs.append(attentions) return outputs # last hidden state, new_mems, (all hidden states), (all attentions) class TFTransfoXLPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = TransfoXLConfig pretrained_model_archive_map = TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP base_model_prefix = "transformer" TRANSFO_XL_START_DOCSTRING = r""" .. note:: TF 2.0 models accepts two formats as inputs: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional arguments. This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: :obj:`model(inputs)`. If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument : - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})` Parameters: config (:class:`~transformers.TransfoXLConfig`): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. """ TRANSFO_XL_INPUTS_DOCSTRING = r""" Args: input_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`transformers.TransfoXLTokenizer`. See :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.encode_plus` for details. `What are input IDs? <../glossary.html#input-ids>`__ mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see `mems` output below). Can be used to speed up sequential decoding. The token ids which have their mems given to this model should not be passed as input ids as they have already been computed. head_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`): Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**. input_embeds (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. """ @add_start_docstrings( "The bare Bert Model transformer outputing raw hidden-states without any specific head on top.", TRANSFO_XL_START_DOCSTRING, ) class TFTransfoXLModel(TFTransfoXLPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFTransfoXLMainLayer(config, name="transformer") @add_start_docstrings_to_callable(TRANSFO_XL_INPUTS_DOCSTRING) def call(self, inputs, **kwargs): r""" Return: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs: last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the last layer of the model. mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: import tensorflow as tf from transformers import TransfoXLTokenizer, TFTransfoXLModel tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') model = TFTransfoXLModel.from_pretrained('transfo-xl-wt103') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1 outputs = model(input_ids) last_hidden_states, mems = outputs[:2] """ outputs = self.transformer(inputs, **kwargs) return outputs class TFTransfoXLLMHead(tf.keras.layers.Layer): def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.input_embeddings = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def call(self, hidden_states): hidden_states = self.input_embeddings(hidden_states, mode="linear") hidden_states = hidden_states + self.bias return hidden_states @add_start_docstrings( """The Transformer-XL Model with a language modeling head on top (adaptive softmax with weights tied to the adaptive input embeddings)""", TRANSFO_XL_START_DOCSTRING, ) class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel): def __init__(self, config): super().__init__(config) self.transformer = TFTransfoXLMainLayer(config, name="transformer") self.sample_softmax = config.sample_softmax assert ( self.sample_softmax <= 0 ), "Sampling from the softmax is not implemented yet. Please look at issue: #3310: https://github.com/huggingface/transformers/issues/3310" self.crit = TFAdaptiveSoftmaxMask( config.vocab_size, config.d_embed, config.d_model, config.cutoffs, div_val=config.div_val, name="crit" ) def get_output_embeddings(self): """ Double-check if you are using adaptive softmax. """ if len(self.crit.out_layers) > 0: return self.crit.out_layers[-1] return None def reset_length(self, tgt_len, ext_len, mem_len): self.transformer.reset_length(tgt_len, ext_len, mem_len) def init_mems(self, bsz): return self.transformer.init_mems(bsz) @add_start_docstrings_to_callable(TRANSFO_XL_INPUTS_DOCSTRING) def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, labels=None, training=False): r""" Return: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs: prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: import tensorflow as tf from transformers import TransfoXLTokenizer, TFTransfoXLLMHeadModel tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') model = TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1 outputs = model(input_ids) prediction_scores, mems = outputs[:2] """ if isinstance(inputs, (tuple, list)): input_ids = inputs[0] mems = inputs[1] if len(inputs) > 1 else mems head_mask = inputs[2] if len(inputs) > 2 else head_mask inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds labels = inputs[4] if len(inputs) > 4 else labels assert len(inputs) <= 5, "Too many inputs." elif isinstance(inputs, dict): input_ids = inputs.get("input_ids") mems = inputs.get("mems", mems) head_mask = inputs.get("head_mask", head_mask) inputs_embeds = inputs.get("inputs_embeds", inputs_embeds) labels = inputs.get("labels", labels) assert len(inputs) <= 5, "Too many inputs." else: input_ids = inputs if input_ids is not None: bsz, tgt_len = shape_list(input_ids)[:2] else: bsz, tgt_len = shape_list(inputs_embeds)[:2] transformer_outputs = self.transformer([input_ids, mems, head_mask, inputs_embeds], training=training) last_hidden = transformer_outputs[0] pred_hid = last_hidden[:, -tgt_len:] outputs = transformer_outputs[1:] softmax_output = self.crit([pred_hid, labels], training=training) outputs = [softmax_output] + outputs return outputs # logits, new_mems, (all hidden states), (all attentions) def prepare_inputs_for_generation(self, inputs, past, **model_kwargs): inputs = {"inputs": inputs} # if past is defined in model kwargs then use it for faster decoding if past: inputs["mems"] = past return inputs