model.py

import torch

from abc import ABC, abstractmethod
from typing import List, Tuple, Optional
from tokenizers import Tokenizer

from text_generation.models.types import Batch, GeneratedText


class Model(ABC):
    def __init__(self, tokenizer: Tokenizer, num_heads: int, device: torch.device):
        self.tokenizer = tokenizer
        self.num_heads = num_heads
        self.device = device

    @abstractmethod
    def forward(self, input_ids, attention_mask, past_key_values: Optional = None) -> Tuple[torch.Tensor, List[Tuple]]:
        raise NotImplementedError

    def generate_token(
            self, batch: Batch
    ) -> Tuple[List[GeneratedText], Optional[Batch]]:
        # For some reason, inference_mode does not work well with GLOO which we use on CPU
        context_manager = (
            torch.no_grad if self.device.type == "cpu" else torch.inference_mode
        )
        with context_manager():
            logits, past = self.forward(**batch.input_ids)

        # List of indices to cache
        next_batch_keep_indices = []

        # New input_ids for next forward
        next_batch_input_ids = []
        next_batch_all_input_ids = []
        next_all_input_lengths = []

        next_batch_size = 0
        next_batch_max_sequence_length = 0

        # Finished requests
        generated_texts: List[GeneratedText] = []

        # Zipped iterator
        iterator = zip(
            batch.requests,
            batch.all_input_lengths,
            logits,
            batch.next_token_choosers,
            batch.stopping_criterias,
            batch.all_input_ids,
        )

        # For each member of the batch
        for i, (
                request,
                input_length,
                logits,
                next_token_chooser,
                stopping_criteria,
                all_tokens,
        ) in enumerate(iterator):
            # Select next token
            next_token = next_token_chooser(all_tokens, logits.unsqueeze(0)[:, -1])

            # Append next token to all tokens
            all_tokens = torch.cat([all_tokens, next_token])

            # Evaluate stopping criteria
            if stopping_criteria(all_tokens):
                # Decode all tokens
                output = self.tokenizer.decode(
                    all_tokens.squeeze(-1), skip_special_tokens=True
                )
                # Add to the list of finished generations with the original request
                generated_texts.append(GeneratedText(request, output, stopping_criteria.current_tokens))
            # add to the next batch
            else:
                next_batch_keep_indices.append(i)
                next_batch_input_ids.append(next_token)
                next_batch_all_input_ids.append(all_tokens)
                next_batch_size += 1
                new_input_length = input_length + 1
                next_all_input_lengths.append(new_input_length)
                next_batch_max_sequence_length = max(
                    next_batch_max_sequence_length, new_input_length
                )

        # We finished all generations in the batch; there is no next batch
        if not next_batch_keep_indices:
            return generated_texts, None

        # If we finished at least one generation
        next_batch_input_ids = {"input_ids": torch.cat(next_batch_input_ids, dim=0)}
        if generated_texts:
            # Apply indices to attention mask, past key values and other items that need to be cached
            next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"][
                next_batch_keep_indices
            ]
            # Force past to be of dim [batch_size, num_heads, ...] for easy indexing
            next_batch_input_ids["past_key_values"] = [
                [t.view(-1, self.num_heads, *t.shape[-2:])[next_batch_keep_indices] for t in layer]
                for layer in past
            ]
            next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
            next_batch_next_token_choosers = [
                batch.next_token_choosers[i] for i in next_batch_keep_indices
            ]
            next_batch_stopping_criterias = [
                batch.stopping_criterias[i] for i in next_batch_keep_indices
            ]
        else:
            next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"]
            next_batch_input_ids["past_key_values"] = past
            next_batch_requests = batch.requests
            next_batch_next_token_choosers = batch.next_token_choosers
            next_batch_stopping_criterias = batch.stopping_criterias

        # Update attention_mask with padding as we added a new token to input_ids
        next_batch_input_ids["attention_mask"] = torch.cat(
            [
                next_batch_input_ids["attention_mask"],
                torch.ones((next_batch_size, 1)).to(self.device),
            ],
            dim=1,
        )

        next_batch = Batch(
            batch_id=batch.batch_id,
            requests=next_batch_requests,
            all_input_lengths=next_all_input_lengths,
            input_ids=next_batch_input_ids,
            all_input_ids=next_batch_all_input_ids,
            next_token_choosers=next_batch_next_token_choosers,
            stopping_criterias=next_batch_stopping_criterias,
            size=next_batch_size,
            max_sequence_length=next_batch_max_sequence_length,
        )
        return generated_texts, next_batch