Upload modeling_p5.py

modeling_p5.py

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+from dataclasses import dataclass
+
+from transformers.models.t5.modeling_t5 import (
+    T5Stack, T5Block, T5LayerNorm, T5LayerSelfAttention, T5LayerFF, T5LayerCrossAttention,
+    T5PreTrainedModel, T5ForConditionalGeneration
+)
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
+import copy
+
+from transformers.modeling_outputs import ModelOutput, BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
+from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+from transformers.utils import logging
+from transformers import BeamScorer, BeamSearchScorer
+
+logger = logging.get_logger(__name__)
+
+# The encoder for input token sequence
+class JointEncoder(T5Stack):
+    def __init__(self, config, embed_tokens=None):
+        super(T5Stack, self).__init__(config)
+        self.config = config
+
+        self.embed_tokens = embed_tokens
+        self.is_decoder = self.config.is_decoder
+        assert self.config.is_decoder is False
+
+        self.block = nn.ModuleList(
+            [T5Block(config, has_relative_attention_bias=(i == 0))
+                for i in range(config.num_layers)]
+        )
+        self.final_layer_norm = T5LayerNorm(
+            config.d_model, eps=config.layer_norm_epsilon)
+        self.dropout = nn.Dropout(config.dropout_rate)
+        
+        ## Set maximum 512 whole words in a source text
+        self.whole_word_embeddings = nn.Embedding(
+            512, config.d_model   ## config.d_model is 768 for base
+        )
+        self.init_weights()
+        self.model_parallel = False
+        self.device_map = None
+
+    def set_input_embeddings(self, new_embeddings):
+        self.embed_tokens = new_embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        whole_word_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        head_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+
+        if inputs_embeds is None:
+            assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
+            inputs_embeds = self.embed_tokens(input_ids) ### embedding step - add HERE ###
+            if whole_word_ids is not None:
+                whole_word_embeds = self.whole_word_embeddings(whole_word_ids)
+                assert whole_word_embeds.shape[-1] == inputs_embeds.shape[-1]
+                inputs_embeds = inputs_embeds + whole_word_embeds
+
+        B, L = inputs_embeds.size()[:-1]
+
+        if attention_mask is None:
+            attention_mask = input_ids.ne(self.config.pad_token_id).to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
+
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask = self.get_extended_attention_mask(
+            attention_mask,
+            (B, L),
+            inputs_embeds.device)
+
+        # initialize past_key_values with `None` if past does not exist
+        if past_key_values is None:
+            past_key_values = [None] * len(self.block)
+
+        # Prepare head mask if needed
+        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
+        present_key_value_states = () if use_cache else None
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        all_cross_attentions = () if (output_attentions and self.is_decoder) else None
+
+        hidden_states = self.dropout(inputs_embeds)
+
+        if self.config.num_layers > 0:
+
+            assert self.block[0].layer[0].SelfAttention.has_relative_attention_bias
+
+            seq_length = L
+            q_len = seq_length
+            k_len = seq_length
+
+            # [1, n_heads, Q_len, K_len]
+            text_position_bias = self.block[0].layer[0].SelfAttention.compute_bias(
+                L, L)
+            num_heads = text_position_bias.size(1)
+            position_bias = text_position_bias.new_zeros(
+                1, num_heads, seq_length, seq_length)
+            position_bias[:, :, :L, :L] = text_position_bias
+
+            position_bias = position_bias + extended_attention_mask
+
+            for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
+                layer_head_mask = head_mask[i]
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask=extended_attention_mask,
+                    position_bias=position_bias,
+                    encoder_hidden_states=None,
+                    encoder_attention_mask=None,
+                    encoder_decoder_position_bias=None,
+                    # head_mask=head_mask[i],
+                    layer_head_mask=layer_head_mask,
+                    past_key_value=past_key_value,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+                
+                # layer_outputs is a tuple with:
+                # hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
+                hidden_states, present_key_value_state = layer_outputs[:2]
+
+                # We share the position biases between the layers - the first layer store them
+                # layer_outputs = hidden-states, key-value-states (self-attention weights),
+                # (self-attention position bias), (cross-attention weights), (cross-attention position bias)
+                
+                # position_bias = layer_outputs[2]
+
+                # append next layer key value states
+                if use_cache:
+                    present_key_value_states = present_key_value_states + \
+                        (present_key_value_state,)
+
+        hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        # Add last layer
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    present_key_value_states,
+                    all_hidden_states,
+                    all_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=present_key_value_states,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class P5(T5ForConditionalGeneration):
+    _keys_to_ignore_on_load_missing = [
+        r"encoder\.embed_tokens\.weight",
+        r"decoder\.embed_tokens\.weight",
+        r"lm_head\.weight",
+    ]
+    _keys_to_ignore_on_load_unexpected = [
+        r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
+    ]
+
+    def __init__(self, config):
+        super(T5ForConditionalGeneration, self).__init__(config)
+
+        self.config = config
+
+        self.model_dim = config.d_model
+
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.use_cache = False
+        encoder_config.is_encoder_decoder = False
+
+        self.encoder = JointEncoder(encoder_config, self.shared)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.is_encoder_decoder = False
+
+        self.decoder = T5Stack(decoder_config, self.shared)
+
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+        self.init_weights()
+
+        self.model_parallel = False
+        self.device_map = None
+
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+        self.decoder.set_input_embeddings(new_embeddings)
+
+    def extend_vocab(self, vocab_size):
+
+        new_shared = nn.Embedding(vocab_size, self.config.d_model)
+        old_weight = self.shared.weight.data.detach().clone()
+        old_vocab_size = old_weight.size(0)
+        new_shared.weight.data[:old_vocab_size, :] = old_weight
+        self.shared = new_shared
+
+        new_lm_head = nn.Linear(self.config.d_model, vocab_size, bias=False)
+        old_weight = self.lm_head.weight.data.detach().clone()
+        old_vocab_size = old_weight.size(0)
+        new_lm_head.weight.data[:old_vocab_size, :] = old_weight
+        self.lm_head = new_lm_head
+
+        self.encoder.embed_tokens = self.shared
+        self.decoder.embed_tokens = self.shared
+
+        self.lm_head.weight = self.shared.weight
+
+        self.config.vocab_size = vocab_size
+        self.encoder.config.vocab_size = vocab_size
+        self.decoder.config.vocab_size = vocab_size
+
+    def forward(
+        self,
+        input_ids=None,
+        whole_word_ids=None,
+        attention_mask=None,
+        encoder_outputs=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        labels=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        head_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        reduce_loss=False,
+
+        return_hidden_state=False,
+
+        **kwargs,
+    ):
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                whole_word_ids=whole_word_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                head_mask=head_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(
+                    encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(
+                    encoder_outputs) > 2 else None,
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
+            # get decoder inputs from shifting lm labels to the right
+            decoder_input_ids = self._shift_right(labels)
+
+        # If decoding with past key value states, only the last tokens
+        # should be given as an input
+        if past_key_values is not None:
+            assert labels is None, "Decoder should not use cached key value states when training."
+            if decoder_input_ids is not None:
+                decoder_input_ids = decoder_input_ids[:, -1:]
+            if decoder_inputs_embeds is not None:
+                decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]
+
+        if attention_mask is None:
+            attention_mask = input_ids.ne(self.config.pad_token_id).to(dtype=hidden_states.dtype, device=hidden_states.device)
+        encoder_attention_mask = attention_mask
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=past_key_values,
+
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = decoder_outputs[0]
+
+        assert self.config.tie_word_embeddings is True
+
+        if self.config.tie_word_embeddings:
+            sequence_output = sequence_output * (self.model_dim ** -0.5)
+
+        if return_hidden_state:
+            return sequence_output
+
+        lm_logits = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            if reduce_loss:
+                loss_fct = CrossEntropyLoss(ignore_index=-100)
+            else:
+                loss_fct = CrossEntropyLoss(ignore_index=-100, reduction='none')
+            loss = loss_fct(
+                lm_logits.view(-1, lm_logits.size(-1)),
+                labels.view(-1))
+
+        return P5Seq2SeqLMOutput(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_last_hidden_state=decoder_outputs.last_hidden_state,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past=None, attention_mask=None, use_cache=None,
+        encoder_outputs=None,
+        **kwargs):
+
+        if past is not None:
+            input_ids = input_ids[:, -1:]
+
+        output = {
+            "decoder_input_ids": input_ids,
+            "past_key_values": past,
+            "encoder_outputs": encoder_outputs,
+            "attention_mask": attention_mask,
+            "use_cache": use_cache,
+        }
+
+        return output
+
+    @staticmethod
+    def _expand_inputs_for_generation(
+        input_ids: torch.LongTensor,
+        expand_size: int = 1,
+        is_encoder_decoder: bool = False,
+        attention_mask: torch.LongTensor = None,
+        encoder_outputs: ModelOutput = None,
+        **model_kwargs
+    ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
+        expanded_return_idx = (
+            torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1,
+                                                                expand_size).view(-1).to(input_ids.device)
+        )
+        input_ids = input_ids.index_select(0, expanded_return_idx)
+
+        if "token_type_ids" in model_kwargs:
+            token_type_ids = model_kwargs["token_type_ids"]
+            model_kwargs["token_type_ids"] = token_type_ids.index_select(
+                0, expanded_return_idx)
+
+        if attention_mask is not None:
+            model_kwargs["attention_mask"] = attention_mask.index_select(
+                0, expanded_return_idx)
+
+        if is_encoder_decoder:
+            assert encoder_outputs is not None
+            encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
+                0, expanded_return_idx
+            )
+            model_kwargs["encoder_outputs"] = encoder_outputs
+
+        return input_ids, model_kwargs
+
+
+@dataclass
+class P5Seq2SeqLMOutput(ModelOutput):
+    """
+    Base class for sequence-to-sequence language models outputs.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Languaged modeling loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`,  with each tensor of shape
+            :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
+
+            Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
+            used (see ``past_key_values`` input) to speed up sequential decoding.
+        decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+        decoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+        encoder_last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder of the model.
+        encoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+        encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    decoder_last_hidden_state: Optional[Tuple[torch.FloatTensor]] = None
+    decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None