Upload t5la_adapter.py

t5la_adapter.py

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+import warnings
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+from transformers import T5ForConditionalGeneration, T5Config, Cache
+from transformers.modeling_outputs import Seq2SeqLMOutput, BaseModelOutput
+
+
+class T5LaAdapterConfig(T5Config):
+    model_type = "t5la_adapter"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "hidden_size": "d_model",
+        "num_attention_heads": "num_heads",
+        "num_hidden_layers": "num_layers",
+        "head_dim": "d_kv",
+    }
+    auto_map = {
+        "AutoConfig": "t5la_adapter.T5LaAdapterConfig",
+        "AutoModel": "t5la_adapter.T5LaAdapterForConditionalGeneration",
+        "AutoModelForSeq2SeqLM": "t5la_adapter.T5LaAdapterForConditionalGeneration",
+        "AutoTokenizer": [
+            "transformers.T5TokenizerFast",
+            "transformers.T5Tokenizer"
+        ]
+    }
+
+    def __init__(
+            self,
+            is_encoder_decoder=True,
+            pad_token_id=0,
+            eos_token_id=1,
+            lookahead_type="la",
+            lookahead_size=0,
+            freeze_base=True,
+            **kwargs,
+    ):
+        self.lookahead_type = lookahead_type
+        self.lookahead_size = lookahead_size
+        self.freeze_base = freeze_base
+        super().__init__(
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            **kwargs,
+        )
+        self.auto_map = {
+            "AutoConfig": "t5la_adapter.T5LaAdapterConfig",
+            "AutoModel": "t5la_adapter.T5LaAdapterForConditionalGeneration",
+            "AutoModelForSeq2SeqLM": "t5la_adapter.T5LaAdapterForConditionalGeneration",
+            "AutoTokenizer": [
+                "transformers.T5TokenizerFast",
+                "transformers.T5Tokenizer"
+            ]
+        }
+
+@dataclass
+class Seq2SeqLMOutputLA(Seq2SeqLMOutput):
+    lookahead_logits: torch.FloatTensor = None
+    lookahead_loss: Optional[torch.FloatTensor] = None
+    base_loss: Optional[torch.FloatTensor] = None
+    decoder_last_hidden_state: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+class LookAheadHeads(nn.Module):
+    def __init__(self, config: T5LaAdapterConfig, k: int) -> None:
+        super().__init__()
+        self.k = k
+        self.heads = nn.ModuleList(
+            [
+                # K heads for LA positions:
+                nn.Linear(config.d_model, config.vocab_size, bias=False)
+                for _ in range(self.k)
+            ]
+        )
+
+    def forward(self, x):
+        # ModuleList can act as an iterable, or be indexed using ints
+        # Apply each head to the shared features
+        logits = [head(x) for head in self.heads]
+
+        # Stack logits along a new dimension to create a tensor of shape [batch_size, num_heads, output_size]
+        if self.k > 0:
+            logits = torch.stack(logits, dim=1)
+        else:
+            logits = logits[0]
+        return logits
+
+
+class T5LaAdapterForConditionalGeneration(T5ForConditionalGeneration):
+    config_class = T5LaAdapterConfig
+    def __init__(self, config: T5LaAdapterConfig):
+        super().__init__(config)
+        if config.lookahead_type == "la":
+            self.la_heads = LookAheadHeads(config, config.lookahead_size)
+        elif config.lookahead_type in ["laa", "laa2"]:
+            self.la_heads = LookAheadHeads(config, 1)
+
+        # Freeze all parameters except the new head
+        if config.freeze_base:
+            for param in self.parameters():
+                param.requires_grad = False
+            for param in self.la_heads.parameters():
+                param.requires_grad = True  # unfreeze the extra head
+
+    def freeze_base(self):
+        # Freeze all parameters except the new head
+        for param in self.parameters():
+            param.requires_grad = False
+        for param in self.la_heads.parameters():
+            param.requires_grad = True  # unfreeze the extra head
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        decoder_attention_mask: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        decoder_head_mask: Optional[torch.FloatTensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        encoder_outputs: Optional[tuple[tuple[torch.Tensor]]] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        lookahead_targets: Optional[torch.LongTensor] = None,
+    ) -> Union[tuple[torch.FloatTensor], Seq2SeqLMOutputLA]:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. T5LA is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [T5LA Training](./t5la#training).
+        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Indices of decoder input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+            T5LA uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5LA
+            Training](./t5la#training).
+        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+            be used by default.
+        decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-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 (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
+            `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
+            labels in `[0, ..., config.vocab_size]`
+        lookahead_targets (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the loss of the LA heads or positions (models of type la, laa, and laa2 have
+            LA heads and lae has LA positions)
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoTokenizer
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
+        >>> config = T5LaAdapterConfig.from_pretrained("google-t5/t5-small", lookahead_size=2)
+        >>> model = T5LaAdapterForConditionalGeneration.from_pretrained("google-t5/t5-small", config=config)
+
+        >>> # training
+        >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
+        >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
+        >>> outputs = model(input_ids=input_ids, labels=labels)
+        >>> loss = outputs.loss
+        >>> logits = outputs.logits
+
+        >>> # inference
+        >>> input_ids = tokenizer(
+        ...     "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
+        ... ).input_ids  # Batch size 1
+        >>> outputs = model.generate(input_ids)
+        >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+        >>> # studies have shown that owning a dog is good for you.
+        ```"""
+        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
+
+        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+        if head_mask is not None and decoder_head_mask is None:
+            if self.config.num_layers == self.config.num_decoder_layers:
+                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+                decoder_head_mask = head_mask
+
+        # Encode if needed (training, first prediction pass)
+        if encoder_outputs is None:
+            # Convert encoder inputs in embeddings if needed
+            encoder_outputs = self.encoder(
+                input_ids=input_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 self.model_parallel:
+            torch.cuda.set_device(self.decoder.first_device)
+
+        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 self.config.lookahead_type == "lae":
+            #  Extend decoder input with lookahead_size extra positions filled by zero as especial tokens:
+            zeros_to_add = torch.zeros(
+                decoder_input_ids.shape[0],
+                self.config.lookahead_size,
+                device=decoder_input_ids.device,
+                dtype=decoder_input_ids.dtype,
+            )
+            decoder_input_ids = torch.cat((decoder_input_ids, zeros_to_add), dim=1)
+            if decoder_attention_mask is not None:
+                ones_to_add = torch.ones(
+                    decoder_attention_mask.shape[0],
+                    self.config.lookahead_size,
+                    device=decoder_attention_mask.device,
+                    dtype=decoder_attention_mask.dtype,
+                )
+                decoder_attention_mask = torch.cat((decoder_attention_mask, ones_to_add), dim=1)
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.decoder.first_device)
+            hidden_states = hidden_states.to(self.decoder.first_device)
+            if decoder_input_ids is not None:
+                decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(self.decoder.first_device)
+            if decoder_attention_mask is not None:
+                decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
+
+        # 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=attention_mask,
+            head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        sequence_output = decoder_outputs[0]
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.encoder.first_device)
+            self.lm_head = self.lm_head.to(self.encoder.first_device)
+            sequence_output = sequence_output.to(self.lm_head.weight.device)
+
+        if self.config.tie_word_embeddings:
+            # Rescale output before projecting on vocab
+            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+            sequence_output = sequence_output * (self.model_dim**-0.5)
+
+        lm_logits = self.lm_head(sequence_output)
+
+        lookahead_logits = None
+        if self.config.lookahead_type == "la":
+            lookahead_logits = self.la_heads(sequence_output)
+        elif self.config.lookahead_type == "laa":
+            la_input = torch.repeat_interleave(hidden_states[:, [-1]], self.config.lookahead_size, dim=1)
+            lookahead_logits = self.la_heads(la_input)
+        elif self.config.lookahead_type == "laa2":
+            lookahead_logits = self.la_heads(hidden_states[:, -self.config.lookahead_size :])
+        elif self.config.lookahead_type == "lae":
+            lookahead_logits = lm_logits[:, -self.config.lookahead_size :].contiguous()
+            lm_logits = lm_logits[:, : -self.config.lookahead_size].contiguous()
+
+        lookahead_loss = None
+        loss = None
+        base_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-100)
+            # move labels to correct device to enable PP
+            labels = labels.to(lm_logits.device)
+            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
+            base_loss = loss.clone()
+            # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
+            if self.config.lookahead_size > 0 and lookahead_targets is not None:
+                lookahead_loss = loss_fct(
+                    lookahead_logits.reshape(-1, lookahead_logits.size(-1)),
+                    lookahead_targets.view(-1),
+                    # vocab_size=self.config.vocab_size,
+                )
+                if self.config.lookahead_type == "la":
+                    # If we simply add, the loss will be larger than a non-LA T5 model because
+                    # in a normal T5, the number of tokens is much lower:
+                    loss = (loss + lookahead_loss) / (1 + self.config.lookahead_size)
+                else:
+                    loss = (loss * lm_logits.shape[1] + lookahead_loss * self.config.lookahead_size) / (
+                        lm_logits.shape[1] + self.config.lookahead_size
+                    )
+
+        if not return_dict:
+            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
+            return ((loss,) + output) if loss is not None else output
+
+        return Seq2SeqLMOutputLA(
+            loss=loss,
+            base_loss=base_loss,
+            logits=lm_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_last_hidden_state=decoder_outputs.last_hidden_state,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+            lookahead_logits=lookahead_logits,
+            lookahead_loss=lookahead_loss,
+        )