# 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. """ from __future__ import annotations from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import tensorflow as tf from ...modeling_tf_utils import ( TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_transfo_xl import TransfoXLConfig from .modeling_tf_transfo_xl_utilities import TFAdaptiveSoftmaxMask logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "transfo-xl-wt103" _CONFIG_FOR_DOC = "TransfoXLConfig" TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = [ "transfo-xl-wt103", # See all Transformer XL models at https://huggingface.co/models?filter=transfo-xl ] 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): self.inv_freq = tf.cast(self.inv_freq, dtype=pos_seq.dtype) 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=0.0, pre_lnorm=False, r_r_bias=None, r_w_bias=None, layer_norm_epsilon=1e-5, init_std=0.02, output_attentions=False, **kwargs, ): super().__init__(**kwargs) self.n_head = n_head self.d_model = d_model self.d_head = d_head self.dropout = dropout self.output_attentions = output_attentions 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, w, r, attn_mask, mems, head_mask, output_attentions, training=False): qlen, rlen, bsz = shape_list(w)[0], shape_list(r)[0], shape_list(w)[1] if mems is not None: mems = tf.cast(mems, dtype=w.dtype) 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_mask_t = tf.cast(attn_mask_t, dtype=attn_score.dtype) attn_score = attn_score * (1.0 - attn_mask_t) - 1e30 * attn_mask_t # [qlen x klen x bsz x n_head] attn_prob = stable_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 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, dropatt=0.0, pre_lnorm=False, r_w_bias=None, r_r_bias=None, layer_norm_epsilon=1e-5, init_std=0.02, output_attentions=False, **kwargs, ): super().__init__(**kwargs) self.dec_attn = TFRelPartialLearnableMultiHeadAttn( n_head, d_model, d_head, dropout, dropatt=dropatt, pre_lnorm=pre_lnorm, r_w_bias=r_w_bias, r_r_bias=r_r_bias, init_std=init_std, layer_norm_epsilon=layer_norm_epsilon, output_attentions=output_attentions, 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, dec_inp, r, dec_attn_mask, mems, head_mask, output_attentions, training=False): attn_outputs = self.dec_attn(dec_inp, r, dec_attn_mask, mems, head_mask, output_attentions, training=training) ff_output = self.pos_ff(attn_outputs[0], training=training) outputs = [ff_output] + attn_outputs[1:] return outputs class TFTransfoEmbeddings(tf.keras.layers.Layer): def __init__(self, vocab_size, emb_size, init_std, **kwargs): super().__init__(**kwargs) self.vocab_size = vocab_size self.emb_size = emb_size self.init_std = init_std def build(self, input_shape): self.weight = self.add_weight( shape=(self.vocab_size, self.emb_size), initializer=get_initializer(self.init_std), name="embeddings", ) super().build(input_shape) def call(self, inputs): return tf.gather(self.weight, inputs) 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( TFTransfoEmbeddings( r_idx - l_idx, d_emb_i, init_std, name=f"emb_layers_._{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=f"emb_projs_._{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.where(mask_i) scatter = tf.scatter_nd(mask_idx, emb_i, shape_list(emb_flat)) emb_flat = tf.cast(emb_flat, dtype=scatter.dtype) emb_flat += scatter 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.config = config self.output_hidden_states = config.output_hidden_states self.output_attentions = config.output_attentions self.return_dict = config.use_return_dict 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.mem_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, 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, layer_norm_epsilon=config.layer_norm_epsilon, init_std=config.init_std, output_attentions=self.output_attentions, name=f"layers_._{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 set_input_embeddings(self, value): raise NotImplementedError def backward_compatible(self): self.sample_softmax = -1 def reset_memory_length(self, mem_len): self.mem_len = mem_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 new_mems = [] end_idx = mlen + tf.math.maximum(0, qlen) beg_idx = tf.math.maximum(0, end_idx - tf.convert_to_tensor(self.mem_len)) for i in range(len(hids)): mems[i] = tf.cast(mems[i], dtype=hids[i].dtype) cat = tf.concat([mems[i], hids[i]], axis=0) tf.stop_gradient(cat) new_mems.append(cat[beg_idx:end_idx]) return new_mems @unpack_inputs def call( self, input_ids: TFModelInputType | None = None, mems: List[tf.Tensor] | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: bool = False, ): # 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 # Compute decoder attention mask all_ones = tf.ones([qlen, klen], dtype=tf.int32) upper_mask = 1 - tf.linalg.band_part(tf.ones([qlen, klen], dtype=tf.int32), -1, mlen) if self.same_length: mask_len = klen - self.mem_len mask_shift_len = qlen - tf.nn.relu(mask_len) # Lazy clamping of negatives to zero # Use an indicator variable instead of a conditional to keep the compiler happy lower_mask = tf.linalg.band_part(all_ones, -1, 0) - ( tf.linalg.band_part(all_ones, mask_shift_len - 1, 0) * tf.cast(mask_shift_len != 0, tf.int32) ) dec_attn_mask = upper_mask + lower_mask else: dec_attn_mask = upper_mask hids = [] attentions = [] if output_attentions else None 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], output_attentions, training=training, ) core_out = layer_outputs[0] if 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] core_out = tf.transpose(core_out, perm=(1, 0, 2)) if output_hidden_states: # Transpose to library standard shape [bsz, len, hidden_dim] and add last layer hids = tuple(tf.transpose(t, perm=(1, 0, 2)) for t in hids) hids = hids + (core_out,) else: hids = None if output_attentions: # Transpose to library standard shape [bsz, n_heads, query_seq_len, key_seq_len] attentions = tuple(tf.transpose(t, perm=(2, 3, 0, 1)) for t in attentions) if not return_dict: return tuple(v for v in [core_out, new_mems, hids, attentions] if v is not None) return TFTransfoXLModelOutput( last_hidden_state=core_out, mems=new_mems, hidden_states=hids, attentions=attentions, ) class TFTransfoXLPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = TransfoXLConfig base_model_prefix = "transformer" @dataclass class TFTransfoXLModelOutput(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. mems (`List[tf.Tensor]` of length `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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(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. """ last_hidden_state: tf.Tensor = None mems: List[tf.Tensor] = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFTransfoXLLMHeadModelOutput(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: losses (`tf.Tensor` of shape *(batch_size, sequence_length-1)*, *optional*, returned when `labels` is provided): Language modeling losses (not reduced). prediction_scores (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax). mems (`List[tf.Tensor]` of length `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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(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. """ prediction_scores: tf.Tensor = None mems: List[tf.Tensor] = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFTransfoXLSequenceClassifierOutputWithPast(ModelOutput): """ Base class for outputs of sentence classification models. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). mems (`List[tf.Tensor]` of length `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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None mems: List[tf.Tensor] = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None TRANSFO_XL_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, 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: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `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: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! Parameters: config ([`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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ TRANSFO_XL_INPUTS_DOCSTRING = r""" Args: input_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) mems (`List[tf.Tensor]` of length `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 (`tf.Tensor` or `Numpy array` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `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. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare Bert Model transformer outputting 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") @unpack_inputs @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFTransfoXLModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, mems: List[tf.Tensor] | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, training: bool = False, ) -> TFTransfoXLModelOutput | Tuple[tf.Tensor]: outputs = self.transformer( input_ids=input_ids, mems=mems, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs @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 _resize_token_embeddings(self, new_num_tokens): raise NotImplementedError() 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_memory_length(self, mem_len): self.transformer.reset_memory_length(mem_len) def init_mems(self, bsz): return self.transformer.init_mems(bsz) @unpack_inputs @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFTransfoXLLMHeadModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, mems: List[tf.Tensor] | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, labels: np.ndarray | tf.Tensor | None = None, training: bool = False, ) -> TFTransfoXLLMHeadModelOutput | Tuple[tf.Tensor]: 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, output_attentions, output_hidden_states, return_dict, training=training, ) last_hidden = transformer_outputs[0] pred_hid = last_hidden[:, -tgt_len:] softmax_output = self.crit(pred_hid, labels, training=training) prediction_scores = softmax_output if labels is None else () if not return_dict: return (prediction_scores,) + transformer_outputs[1:] return TFTransfoXLLMHeadModelOutput( prediction_scores=prediction_scores, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **model_kwargs): inputs = {} # if past is defined in model kwargs then use it for faster decoding if past_key_values: input_ids = tf.expand_dims(input_ids[:, -1], axis=-1) else: input_ids = input_ids return inputs @add_start_docstrings( """ The Transfo XL Model transformer with a sequence classification head on top (linear layer). [`TFTransfoXLForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1,GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, TRANSFO_XL_START_DOCSTRING, ) class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.score = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.init_range), name="score", use_bias=False, ) self.transformer = TFTransfoXLMainLayer(config, name="transformer") def get_output_embeddings(self): # Remove after transformers v4.32. Fix this model's `test_model_common_attributes` test too. logger.warning( "Sequence classification models do not have output embeddings. `.get_output_embeddings` will be removed " "in transformers v4.32." ) return self.transformer.word_emb @unpack_inputs @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFTransfoXLSequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, mems: List[tf.Tensor] | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[Tuple, TFTransfoXLSequenceClassifierOutputWithPast]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ transformer_outputs = self.transformer( input_ids=input_ids, mems=mems, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) in_logits = None if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = ( tf.argmax(tf.cast(tf.math.equal(input_ids, self.config.pad_token_id), input_ids.dtype), axis=-1) - 1 ) sequence_lengths = tf.where(sequence_lengths >= 0, sequence_lengths, input_ids.shape[-1] - 1) in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1) else: sequence_lengths = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) loss = None if labels is not None: if input_ids is not None: batch_size, sequence_length = shape_list(input_ids)[:2] else: batch_size, sequence_length = shape_list(inputs_embeds)[:2] assert ( self.config.pad_token_id is not None or batch_size == 1 ), "Cannot handle batch sizes > 1 if no padding token is defined." if not tf.is_tensor(sequence_lengths): in_logits = logits[0:batch_size, sequence_lengths] loss = self.hf_compute_loss(tf.reshape(labels, [-1, 1]), tf.reshape(in_logits, [-1, self.num_labels])) pooled_logits = in_logits if in_logits is not None else logits if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TFTransfoXLSequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, )