# coding=utf-8 # Copyright 2021 The Facebook, Inc and The HuggingFace Inc. team. 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 Blenderbot model. """ import os import random import warnings from typing import Dict, Optional, Tuple, Union import tensorflow as tf from ...activations_tf import get_tf_activation from ...file_utils import ( add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from ...modeling_tf_outputs import ( TFBaseModelOutput, TFBaseModelOutputWithPastAndCrossAttentions, TFSeq2SeqLMOutput, TFSeq2SeqModelOutput, ) # Public API from ...modeling_tf_utils import ( DUMMY_INPUTS, TFCausalLanguageModelingLoss, TFPreTrainedModel, TFSharedEmbeddings, TFWrappedEmbeddings, input_processing, keras_serializable, shape_list, ) from ...utils import logging from .configuration_blenderbot import BlenderbotConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/blenderbot-400M-distill" _CONFIG_FOR_DOC = "BlenderbotConfig" _TOKENIZER_FOR_DOC = "BlenderbotTokenizer" LARGE_NEGATIVE = -1e8 # Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int): start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id) shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = tf.where( shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids ) if tf.executing_eagerly(): # "Verify that `labels` has only positive values and -100" assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0)) # Make sure the assertion op is called by wrapping the result in an identity no-op with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids) return shifted_input_ids # Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE mask_cond = tf.range(shape_list(mask)[-1]) mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask) if past_key_values_length > 0: mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1) return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1)) # Copied from transformers.models.bart.modeling_tf_bart._expand_mask def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None, past_key_values_length: int = 0): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ src_len = shape_list(mask)[1] tgt_len = tgt_len if tgt_len is not None else src_len one_cst = tf.constant(1.0) mask = tf.cast(mask, dtype=one_cst.dtype) expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1)) return (one_cst - expanded_mask) * LARGE_NEGATIVE class TFBlenderbotLearnedPositionalEmbedding(TFSharedEmbeddings): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs): super().__init__(num_embeddings, embedding_dim, **kwargs) def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0): """Input is expected to be of size [bsz x seqlen].""" bsz, seq_len = input_shape[:2] positions = tf.range(past_key_values_length, seq_len + past_key_values_length, delta=1, name="range") return super().call(positions) # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->Blenderbot class TFBlenderbotAttention(tf.keras.layers.Layer): """Multi-headed attention from "Attention Is All You Need""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, **kwargs, ): super().__init__(**kwargs) self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = tf.keras.layers.Dropout(dropout) self.head_dim = embed_dim // num_heads assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" self.scaling = self.head_dim ** -0.5 self.is_decoder = is_decoder self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj") self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj") self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj") self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj") def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int): return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3)) def call( self, hidden_states: tf.Tensor, key_value_states: Optional[tf.Tensor] = None, past_key_value: Optional[Tuple[Tuple[tf.Tensor]]] = None, attention_mask: Optional[tf.Tensor] = None, layer_head_mask: Optional[tf.Tensor] = None, training=False, ) -> Tuple[tf.Tensor, Optional[tf.Tensor]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, embed_dim = shape_list(hidden_states) # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = tf.concat([past_key_value[0], key_states], axis=2) value_states = tf.concat([past_key_value[1], value_states], axis=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape) key_states = tf.reshape(key_states, proj_shape) value_states = tf.reshape(value_states, proj_shape) src_len = shape_list(key_states)[1] attn_weights = tf.matmul(query_states, key_states, transpose_b=True) # The tf.debugging asserts are not compliant with XLA then they # have to be disabled in other modes than eager. if tf.executing_eagerly(): tf.debugging.assert_equal( shape_list(attn_weights), [bsz * self.num_heads, tgt_len, src_len], message=f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {shape_list(attn_weights)}", ) if attention_mask is not None: # The tf.debugging asserts are not compliant with XLA then they # have to be disabled in other modes than eager. if tf.executing_eagerly(): tf.debugging.assert_equal( shape_list(attention_mask), [bsz, 1, tgt_len, src_len], message=f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}", ) attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype) attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) attn_weights = tf.nn.softmax(attn_weights, axis=-1) if layer_head_mask is not None: # The tf.debugging asserts are not compliant with XLA then they # have to be disabled in other modes than eager. if tf.executing_eagerly(): tf.debugging.assert_equal( shape_list(layer_head_mask), [self.num_heads], message=f"Head mask for a single layer should be of size {(self.num_heads)}, but is {shape_list(layer_head_mask)}", ) attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape( attn_weights, (bsz, self.num_heads, tgt_len, src_len) ) attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) attn_probs = self.dropout(attn_weights, training=training) attn_output = tf.matmul(attn_probs, value_states) # The tf.debugging asserts are not compliant with XLA then they # have to be disabled in other modes than eager. if tf.executing_eagerly(): tf.debugging.assert_equal( shape_list(attn_output), [bsz * self.num_heads, tgt_len, self.head_dim], message=f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {shape_list(attn_output)}", ) attn_output = tf.transpose( tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3) ) attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim)) attn_output = self.out_proj(attn_output) attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) return attn_output, attn_weights, past_key_value # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartEncoderLayer with MBart->Blenderbot class TFBlenderbotEncoderLayer(tf.keras.layers.Layer): def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.d_model self.self_attn = TFBlenderbotAttention( self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn" ) self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") self.dropout = tf.keras.layers.Dropout(config.dropout) self.activation_fn = get_tf_activation(config.activation_function) self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout) self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1") self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2") self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training=False): """ Args: hidden_states (:obj:`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (:obj:`tf.Tensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (:obj:`tf.Tensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)` """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) hidden_states, self_attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask ) # The tf.debugging asserts are not compliant with XLA then they # have to be disabled in other modes than eager. if tf.executing_eagerly(): tf.debugging.assert_equal( shape_list(hidden_states), shape_list(residual), message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}", ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout(hidden_states, training=training) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states return hidden_states, self_attn_weights # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer with MBart->Blenderbot class TFBlenderbotDecoderLayer(tf.keras.layers.Layer): def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.d_model self.self_attn = TFBlenderbotAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, name="self_attn", is_decoder=True, ) self.dropout = tf.keras.layers.Dropout(config.dropout) self.activation_fn = get_tf_activation(config.activation_function) self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout) self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") self.encoder_attn = TFBlenderbotAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, name="encoder_attn", is_decoder=True, ) self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm") self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1") self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2") self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") def call( self, hidden_states, attention_mask: Optional[tf.Tensor] = None, encoder_hidden_states: Optional[tf.Tensor] = None, encoder_attention_mask: Optional[tf.Tensor] = None, layer_head_mask: Optional[tf.Tensor] = None, cross_attn_layer_head_mask: Optional[tf.Tensor] = None, past_key_value: Optional[Tuple[tf.Tensor]] = None, training=False, ) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]: """ Args: hidden_states (:obj:`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (:obj:`tf.Tensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (:obj:`tf.Tensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)` encoder_attention_mask (:obj:`tf.Tensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (:obj:`tf.Tensor`): mask for attention heads in a given layer of size `(decoder_attention_heads,)` cross_attn_layer_head_mask (:obj:`tf.Tensor`): mask for heads of the cross-attention module. `(decoder_attention_heads,)` past_key_value (:obj:`Tuple(tf.Tensor)`): cached past key and value projection states """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, ) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout(hidden_states, training=training) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = residual + hidden_states return ( hidden_states, self_attn_weights, cross_attn_weights, present_key_value, ) class TFBlenderbotPreTrainedModel(TFPreTrainedModel): config_class = BlenderbotConfig base_model_prefix = "model" @property def dummy_inputs(self): pad_token = 1 input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32) decoder_input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32) dummy_inputs = { "decoder_input_ids": decoder_input_ids, "attention_mask": tf.math.not_equal(input_ids, pad_token), "input_ids": input_ids, } return dummy_inputs @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"), "decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"), "decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"), } ] ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.serving def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) BLENDERBOT_START_DOCSTRING = r""" This model inherits from :class:`~transformers.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 `__ 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. .. 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 :meth:`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 :obj:`input_ids` only and nothing else: :obj:`model(input_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})` Args: config (:class:`~transformers.BlenderbotConfig`): 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.TFPreTrainedModel.from_pretrained` method to load the model weights. """ BLENDERBOT_GENERATION_EXAMPLE = r""" Conversation example:: >>> from transformers import BlenderbotTokenizer, TFBlenderbotForConditionalGeneration >>> mname = 'facebook/blenderbot-400M-distill' >>> model = TFBlenderbotForConditionalGeneration.from_pretrained(mname) >>> tokenizer = BlenderbotTokenizer.from_pretrained(mname) >>> UTTERANCE = "My friends are cool but they eat too many carbs." >>> print("Human: ", UTTERANCE) >>> inputs = tokenizer([UTTERANCE], return_tensors='tf') >>> reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]) >>> REPLY = "I'm not sure" >>> print("Human: ", REPLY) >>> NEXT_UTTERANCE = ( ... "My friends are cool but they eat too many carbs. That's unfortunate. " ... "Are they trying to lose weight or are they just trying to be healthier? " ... " I'm not sure." ... ) >>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors='tf') >>> next_reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0]) """ BLENDERBOT_INPUTS_DOCSTRING = r""" Args: input_ids (:obj:`tf.Tensor` of shape :obj:`({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`~transformers.BlenderbotTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. `What are input IDs? <../glossary.html#input-ids>`__ attention_mask (:obj:`tf.Tensor` of shape :obj:`({0})`, `optional`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. `What are attention masks? <../glossary.html#attention-mask>`__ decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using :class:`~transformers.BlenderbotTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. `What are decoder input IDs? <../glossary.html#decoder-input-ids>`__ Blenderbot uses the :obj:`bos_token_id` as the starting token for :obj:`decoder_input_ids` generation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_input_ids` have to be input (see :obj:`past_key_values`). decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`): will be made by default and ignore pad tokens. It is not recommended to set this for most use cases. head_mask (:obj:`tf.Tensor` of shape :obj:`(encoder_layers, encoder_attention_heads)`, `optional`): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (:obj:`tf.Tensor` of shape :obj:`(decoder_layers, decoder_attention_heads)`, `optional`): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (:obj:`tf.Tensor` of shape :obj:`(decoder_layers, decoder_attention_heads)`, `optional`): Mask to nullify selected heads of the cross-attention modules. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (:obj:`tf.FloatTensor`, `optional`): hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of past_key_values (:obj:`Tuple[Tuple[tf.Tensor]]` of length :obj:`config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). Set to :obj:`False` during training, :obj:`True` during generation output_attentions (:obj:`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 (:obj:`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 (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.file_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 (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @keras_serializable class TFBlenderbotEncoder(tf.keras.layers.Layer): config_class = BlenderbotConfig """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a :class:`TFBlenderbotEncoderLayer`. Args: config: BlenderbotConfig """ def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[TFSharedEmbeddings] = None, **kwargs): super().__init__(**kwargs) self.config = config self.dropout = tf.keras.layers.Dropout(config.dropout) self.layerdrop = config.encoder_layerdrop self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0 self.embed_tokens = embed_tokens self.embed_positions = TFBlenderbotLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, name="embed_positions", ) self.layers = [TFBlenderbotEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)] self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") def get_embed_tokens(self): return self.embed_tokens def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens def call( self, input_ids=None, inputs_embeds=None, attention_mask=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): """ Args: input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using :class:`~transformers.BlenderbotTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. `What are input IDs? <../glossary.html#input-ids>`__ attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. `What are attention masks? <../glossary.html#attention-mask>`__ head_mask (:obj:`tf.Tensor` of shape :obj:`(encoder_layers, encoder_attention_heads)`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): 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 :obj:`input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (:obj:`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 (:obj:`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 (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.file_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 (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif inputs["input_ids"] is not None: input_shape = shape_list(inputs["input_ids"]) elif inputs["inputs_embeds"] is not None: input_shape = shape_list(inputs["inputs_embeds"])[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs["inputs_embeds"] is None: inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"]) * self.embed_scale embed_pos = self.embed_positions(input_shape) hidden_states = inputs["inputs_embeds"] + embed_pos hidden_states = self.dropout(hidden_states, training=inputs["training"]) # check attention mask and invert if inputs["attention_mask"] is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(inputs["attention_mask"]) else: attention_mask = None encoder_states = () if inputs["output_hidden_states"] else None all_attentions = () if inputs["output_attentions"] else None # check if head_mask has a correct number of layers specified if desired # The tf.debugging asserts are not compliant with XLA then they # have to be disabled in other modes than eager. if inputs["head_mask"] is not None and tf.executing_eagerly(): tf.debugging.assert_equal( shape_list(inputs["head_mask"])[0], len(self.layers), message=f"The head_mask should be specified for {len(self.layers)} layers, but it is for {shape_list(inputs['head_mask'])[0]}.", ) # encoder layers for idx, encoder_layer in enumerate(self.layers): if inputs["output_hidden_states"]: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if inputs["training"] and (dropout_probability < self.layerdrop): # skip the layer continue hidden_states, attn = encoder_layer( hidden_states, attention_mask, inputs["head_mask"][idx] if inputs["head_mask"] is not None else None, ) if inputs["output_attentions"]: all_attentions += (attn,) hidden_states = self.layer_norm(hidden_states) if inputs["output_hidden_states"]: encoder_states = encoder_states + (hidden_states,) if not inputs["return_dict"]: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) @keras_serializable class TFBlenderbotDecoder(tf.keras.layers.Layer): config_class = BlenderbotConfig """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`TFBlenderbotDecoderLayer` Args: config: BlenderbotConfig embed_tokens: output embedding """ def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[TFSharedEmbeddings] = None, **kwargs): super().__init__(**kwargs) self.config = config self.padding_idx = config.pad_token_id self.embed_tokens = embed_tokens self.layerdrop = config.decoder_layerdrop self.embed_positions = TFBlenderbotLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, name="embed_positions", ) self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0 self.layers = [TFBlenderbotDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)] self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") self.dropout = tf.keras.layers.Dropout(config.dropout) def get_embed_tokens(self): return self.embed_tokens def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens def call( self, input_ids=None, inputs_embeds=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): r""" Args: input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using :class:`~transformers.BlenderbotTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. `What are input IDs? <../glossary.html#input-ids>`__ attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. `What are attention masks? <../glossary.html#attention-mask>`__ encoder_hidden_states (:obj:`tf.Tensor` of shape :obj:`(batch_size, encoder_sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, encoder_sequence_length)`, `optional`): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. `What are attention masks? <../glossary.html#attention-mask>`__ head_mask (:obj:`tf.Tensor` of shape :obj:`(decoder_layers, decoder_attention_heads)`, `optional`): Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (:obj:`tf.Tensor` of shape :obj:`(decoder_layers, decoder_attention_heads)`, `optional`): Mask to nullify selected heads of the cross-attention modules. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (:obj:`Tuple[Tuple[tf.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size, sequence_length)`. inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): 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 :obj:`input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (:obj:`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 (:obj:`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 (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.file_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 (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif inputs["input_ids"] is not None: input_shape = shape_list(inputs["input_ids"]) elif inputs["inputs_embeds"] is not None: input_shape = shape_list(inputs["inputs_embeds"])[:-1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") past_key_values_length = ( shape_list(inputs["past_key_values"][0][0])[2] if inputs["past_key_values"] is not None else 0 ) # embed positions positions = self.embed_positions(input_shape, past_key_values_length) if inputs["inputs_embeds"] is None: inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"]) * self.embed_scale hidden_states = inputs["inputs_embeds"] # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length) else: combined_attention_mask = _expand_mask( tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1] ) if inputs["attention_mask"] is not None: combined_attention_mask = combined_attention_mask + _expand_mask( inputs["attention_mask"], tgt_len=input_shape[-1] ) if inputs["encoder_hidden_states"] is not None and inputs["encoder_attention_mask"] is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] inputs["encoder_attention_mask"] = _expand_mask(inputs["encoder_attention_mask"], tgt_len=input_shape[-1]) hidden_states = hidden_states + positions hidden_states = self.dropout(hidden_states, training=inputs["training"]) # decoder layers all_hidden_states = () if inputs["output_hidden_states"] else None all_self_attns = () if inputs["output_attentions"] else None all_cross_attns = () if (inputs["output_attentions"] and inputs["encoder_hidden_states"] is not None) else None present_key_values = () if inputs["use_cache"] else None # check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired # The tf.debugging asserts are not compliant with XLA then they # have to be disabled in other modes than eager. for attn_mask in ["head_mask", "cross_attn_head_mask"]: if inputs[attn_mask] is not None and tf.executing_eagerly(): tf.debugging.assert_equal( shape_list(inputs[attn_mask])[0], len(self.layers), message=f"The {attn_mask} should be specified for {len(self.layers)} layers, but it is for {shape_list(inputs[attn_mask])[0]}.", ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if inputs["output_hidden_states"]: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if inputs["training"] and (dropout_probability < self.layerdrop): continue past_key_value = inputs["past_key_values"][idx] if inputs["past_key_values"] is not None else None hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer( hidden_states, attention_mask=combined_attention_mask, encoder_hidden_states=inputs["encoder_hidden_states"], encoder_attention_mask=inputs["encoder_attention_mask"], layer_head_mask=inputs["head_mask"][idx] if inputs["head_mask"] is not None else None, cross_attn_layer_head_mask=inputs["cross_attn_head_mask"][idx] if inputs["cross_attn_head_mask"] is not None else None, past_key_value=past_key_value, ) if inputs["use_cache"]: present_key_values += (present_key_value,) if inputs["output_attentions"]: all_self_attns += (layer_self_attn,) if inputs["encoder_hidden_states"] is not None: all_cross_attns += (layer_cross_attn,) hidden_states = self.layer_norm(hidden_states) if inputs["output_hidden_states"]: all_hidden_states += (hidden_states,) if inputs["output_attentions"]: all_self_attns = list(all_self_attns) if inputs["encoder_hidden_states"] is not None: all_cross_attns = list(all_cross_attns) if inputs["use_cache"]: present_key_values = (inputs["encoder_hidden_states"], present_key_values) if not inputs["return_dict"]: return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns else: return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attns, ) @keras_serializable class TFBlenderbotMainLayer(tf.keras.layers.Layer): config_class = BlenderbotConfig def __init__(self, config: BlenderbotConfig, **kwargs): super().__init__(**kwargs) self.config = config self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared") with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name: pass # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope. embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name) embed_tokens.vocab_size = self.shared.vocab_size embed_tokens.hidden_size = self.shared.hidden_size self.encoder = TFBlenderbotEncoder(config, embed_tokens, name="encoder") self.decoder = TFBlenderbotDecoder(config, embed_tokens, name="decoder") def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared.weight = new_embeddings self.shared.vocab_size = self.shared.weight.shape[0] # retrieve correct absolute scope for embed token wrapper with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name: pass # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope. embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name) self.encoder.set_embed_tokens(embed_tokens) self.decoder.set_embed_tokens(embed_tokens) def call( self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs ): inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) inputs["output_hidden_states"] = ( inputs["output_hidden_states"] if inputs["output_hidden_states"] is not None else self.config.output_hidden_states ) if inputs["encoder_outputs"] is None: inputs["encoder_outputs"] = self.encoder( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], head_mask=inputs["head_mask"], inputs_embeds=inputs["inputs_embeds"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True elif inputs["return_dict"] and not isinstance(inputs["encoder_outputs"], TFBaseModelOutput): inputs["encoder_outputs"] = TFBaseModelOutput( last_hidden_state=inputs["encoder_outputs"][0], hidden_states=inputs["encoder_outputs"][1] if len(inputs["encoder_outputs"]) > 1 else None, attentions=inputs["encoder_outputs"][2] if len(inputs["encoder_outputs"]) > 2 else None, ) # If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False elif not inputs["return_dict"] and not isinstance(inputs["encoder_outputs"], tuple): inputs["encoder_outputs"] = inputs["encoder_outputs"].to_tuple() decoder_outputs = self.decoder( inputs["decoder_input_ids"], attention_mask=inputs["decoder_attention_mask"], encoder_hidden_states=inputs["encoder_outputs"][0], encoder_attention_mask=inputs["attention_mask"], head_mask=inputs["decoder_head_mask"], cross_attn_head_mask=inputs["cross_attn_head_mask"], past_key_values=inputs["past_key_values"], inputs_embeds=inputs["decoder_inputs_embeds"], use_cache=inputs["use_cache"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) if not inputs["return_dict"]: return decoder_outputs + inputs["encoder_outputs"] return TFSeq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state, encoder_hidden_states=inputs["encoder_outputs"].hidden_states, encoder_attentions=inputs["encoder_outputs"].attentions, ) @add_start_docstrings( "The bare BLENDERBOT Model outputting raw hidden-states without any specific head on top.", BLENDERBOT_START_DOCSTRING, ) class TFBlenderbotModel(TFBlenderbotPreTrainedModel): def __init__(self, config: BlenderbotConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.model = TFBlenderbotMainLayer(config, name="model") def get_encoder(self): return self.model.encoder def get_decoder(self): return self.model.decoder @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): if pretrained_model_name_or_path == "facebook/blenderbot-90M": from ..blenderbot_small import TFBlenderbotSmallModel warnings.warn( "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical checkpoint `facebook/small_blenderbot-90M` with `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')` instead.", FutureWarning, ) return TFBlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs ): inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) outputs = self.model( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], decoder_input_ids=inputs["decoder_input_ids"], decoder_attention_mask=inputs["decoder_attention_mask"], head_mask=inputs["head_mask"], decoder_head_mask=inputs["decoder_head_mask"], cross_attn_head_mask=inputs["cross_attn_head_mask"], encoder_outputs=inputs["encoder_outputs"], past_key_values=inputs["past_key_values"], inputs_embeds=inputs["inputs_embeds"], decoder_inputs_embeds=inputs["decoder_inputs_embeds"], use_cache=inputs["use_cache"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) return outputs # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None return TFSeq2SeqModelOutput( last_hidden_state=output.last_hidden_state, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, cross_attentions=cross_attns, encoder_last_hidden_state=output.encoder_last_hidden_state, encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, ) @add_start_docstrings( "The BLENDERBOT Model with a language modeling head. Can be used for summarization.", BLENDERBOT_START_DOCSTRING, ) class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausalLanguageModelingLoss): _keys_to_ignore_on_load_unexpected = [ r"model.encoder.embed_tokens.weight", r"model.decoder.embed_tokens.weight", ] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.model = TFBlenderbotMainLayer(config, name="model") self.use_cache = config.use_cache # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency. self.final_logits_bias = self.add_weight( name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False ) def get_decoder(self): return self.model.decoder def get_encoder(self): return self.model.encoder def get_output_embeddings(self): return self.get_input_embeddings() def set_output_embeddings(self, value): self.set_input_embeddings(value) def get_bias(self): return {"final_logits_bias": self.final_logits_bias} def set_bias(self, value): self.final_logits_bias = value["final_logits_bias"] @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): if pretrained_model_name_or_path == "facebook/blenderbot-90M": from ..blenderbot_small import TFBlenderbotSmallForConditionalGeneration warnings.warn( "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical checkpoint `facebook/small_blenderbot-90M` with `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')` instead.", FutureWarning, ) return TFBlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name_or_path) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE) def call( self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs: Optional[TFBaseModelOutput] = None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, **kwargs, ): r""" labels (:obj:`tf.tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the masked language modeling loss. Indices should either be in ``[0, ..., config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``. Returns: """ inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, labels=labels, training=training, kwargs_call=kwargs, ) if inputs["labels"] is not None: inputs["labels"] = tf.where( inputs["labels"] == self.config.pad_token_id, tf.fill(shape_list(inputs["labels"]), -100), inputs["labels"], ) inputs["use_cache"] = False if inputs["decoder_input_ids"] is None: inputs["decoder_input_ids"] = shift_tokens_right( inputs["labels"], self.config.pad_token_id, self.config.decoder_start_token_id ) outputs = self.model( inputs["input_ids"], attention_mask=inputs["attention_mask"], decoder_input_ids=inputs["decoder_input_ids"], encoder_outputs=inputs["encoder_outputs"], decoder_attention_mask=inputs["decoder_attention_mask"], head_mask=inputs["head_mask"], decoder_head_mask=inputs["decoder_head_mask"], cross_attn_head_mask=inputs["cross_attn_head_mask"], past_key_values=inputs["past_key_values"], inputs_embeds=inputs["inputs_embeds"], decoder_inputs_embeds=inputs["decoder_inputs_embeds"], use_cache=inputs["use_cache"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) lm_logits = self.model.shared(outputs[0], mode="linear") lm_logits = lm_logits + self.final_logits_bias masked_lm_loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], lm_logits) if not inputs["return_dict"]: output = (lm_logits,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return TFSeq2SeqLMOutput( loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, # index 1 of d outputs decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs cross_attentions=outputs.cross_attentions, # index 4 of d outputs encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out encoder_attentions=outputs.encoder_attentions, # 2 of e out ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None return TFSeq2SeqLMOutput( logits=output.logits, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, cross_attentions=cross_attns, encoder_last_hidden_state=output.encoder_last_hidden_state, encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, ) # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation def prepare_inputs_for_generation( self, decoder_input_ids, past, attention_mask, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, **kwargs, ) -> Dict: assert past is not None and len(past) in {1, 2}, f"past has to be an iterable of length 1,2 got {past}" if len(past) == 1: assert isinstance(past[0], tf.Tensor), f"`past[0]` has to be of type `tf.Tensor`, but is {type(past[0])}" encoder_outputs = TFBaseModelOutput(last_hidden_state=past[0]) past_key_values = None else: assert ( len(past) == 2 ), "`past` has to be of length 2 with the encoder_outputs at the first position and past_key_values at the second position." encoder_outputs, past_key_values = past if isinstance(encoder_outputs, tuple): assert isinstance( encoder_outputs[0], tf.Tensor ), f"`encoder_outputs[0]` has to be of type `tf.Tensor`, but is {type(encoder_outputs[0])}" encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_outputs[0]) elif isinstance(encoder_outputs, tf.Tensor): encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_outputs) assert ( past_key_values ), f"decoder cached states must be truthy. got {past_key_values} from the 2nd element of past" decoder_input_ids = decoder_input_ids[:, -1:] assert isinstance( encoder_outputs, TFBaseModelOutput ), f"encoder_outputs should be a TFBaseModelOutput, Instead got {type(encoder_outputs)}." return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, # change this to avoid caching (presumably for debugging) } @staticmethod # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration._reorder_cache def _reorder_cache(past, beam_idx): if len(past) == 1: return past past_key_values = past[1] reordered_past = () for layer_past_key_values in past_key_values: reordered_past += ( tuple(tf.gather(layer_past_key_value, beam_idx) for layer_past_key_value in layer_past_key_values[:2]) + layer_past_key_values[2:], ) return (past[0], reordered_past)