rm deprecated cls (removed dep)

Author: yuanheng-zhao

colossalai/inference/struct.py

@@ -1,11 +1,9 @@
 import enum
 from dataclasses import dataclass
-from typing import Any, List, Tuple, Union
+from typing import Any, List, Union
 
 import torch
-from ordered_set import OrderedSet
 
-from colossalai.inference.flash_decoding_utils import FDIntermTensors
 from colossalai.logging import get_dist_logger
 
 logger = get_dist_logger(__name__)
@@ -170,230 +168,6 @@ def __repr__(self) -> str:
         )
 
 
-@dataclass
-class BatchInfo:
-    """
-    Information to be passed and used for a batch of sequences.
-    """
-
-    max_batch_size: int
-    kv_max_split_num: int
-    num_heads: int
-    head_dim: int
-    sequences_set: OrderedSet[Sequence] = None
-    is_prompts: bool = True
-    device: torch.device = None
-    dtype: torch.dtype = None
-    fd_inter_tensor: FDIntermTensors = None
-
-    def __post_init__(self):
-        if self.device is None:
-            self.device = torch.cuda.current_device()
-        if self.sequences_set is None:
-            self.sequences_set = OrderedSet()
-        if self.fd_inter_tensor is None:
-            self.fd_inter_tensor = FDIntermTensors()
-
-    def init_fd_tensors(self):
-        if not self.fd_inter_tensor.is_initialized:
-            self.fd_inter_tensor.initialize(
-                max_batch_size=self.max_batch_size,
-                num_attn_heads=self.num_heads,
-                kv_max_split_num=self.kv_max_split_num,
-                head_dim=self.head_dim,
-                dtype=self.dtype,
-                device=self.device,
-            )
-
-    def get_block_table_tensor(self) -> None:
-        tesnor_list = []
-        block_table = None
-
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        for seq in self.sequences_set:
-            block_table = seq.block_table
-            assert (
-                block_table is not None
-            ), f"The sequence(request_id {seq.request_id}) has not initialized the block_table."
-            tesnor_list.append(seq.block_table)
-
-        block_table = torch.stack(tesnor_list)
-        return block_table
-
-    def clear_batch(self) -> None:
-        """
-        Clear sequence set and block table if we need to abort this batch.
-            Prefill: clear sequence set and move them to running batch(external)
-            Decoding: mark unfinished sequences as aborted.
-        """
-        if self.is_prompts:
-            self.sequences_set.clear()
-        else:
-            for seq in self.sequences_set:
-                seq.mark_aborted()
-                if seq.check_finish():
-                    seq.mark_finished()
-
-            self.sequences_set.clear()
-
-    def fliter_batch(self) -> List["Sequence"]:
-        """
-        Remove completed sentences from a batch.
-
-        Returns:
-            List["Sequence"]: List of finished sequences.
-        """
-        finish_seqs = []
-        for seq in self.sequences_set:
-            if seq.check_finish():
-                finish_seqs.append(seq)
-        for finish_seq in finish_seqs:
-            self.sequences_set.discard(finish_seq)
-        return finish_seqs
-
-    def abort_seq(self, seq: "Sequence") -> "Sequence":
-        """
-        Remove sequence from the batch.
-        """
-        if not seq.check_finish():
-            seq.status = RequestStatus.ABORTED
-        self.sequences_set.discard(seq)
-        return seq
-
-    def add_seqs(self, seqs: Union[Sequence, List[Sequence]]) -> None:
-        """
-        Add new sequence to batch
-
-        Args:
-            seqs (List["Sequence"]): The list of new sequences.
-        """
-        # covnert single sequence to list
-        if isinstance(seqs, Sequence):
-            seqs = [seqs]
-
-        for seq in seqs:
-            if seq in self.sequences_set:
-                logger.warning(f"The sequence(request_id {seq.request_id}) is already in sequences_set.")
-                continue
-            self.sequences_set.add(seq)
-
-    def del_seq(self, seq: Sequence) -> Sequence:
-        """
-        Delete sequence in batch
-        """
-        self.sequences_set.discard(seq)
-
-    @property
-    def is_empty(self) -> None:
-        """
-        Check whether sequences_set is empty.
-        """
-        return not self.sequences_set
-
-    def update_batch_tokens(self, tokens: Union[List[int], List[List[int]], torch.Tensor]) -> None:
-        """
-        Add an output token for each sentence in the batch.
-
-        Args:
-            tokens (List[int]): A batch of tokens
-        """
-
-        if isinstance(tokens, torch.Tensor):
-            tokens = tokens.tolist()
-
-        assert self.get_batch_size() == len(tokens), "The number of tokens does not match batch_size."
-
-        for seq, token in zip(self.sequences_set, tokens):
-            if not isinstance(token, list):
-                if not isinstance(token, int):
-                    raise TypeError(f"The token type must be List[int] or int, but got {type(token)}.")
-                token = [token]
-            seq.output_token_id += token
-            seq.check_finish()
-
-    def get_batch_size(self) -> int:
-        """
-        Get batch_size of this batch
-        """
-        return len(self.sequences_set)
-
-    def get_batch_inputs(self) -> torch.LongTensor:
-        """
-        Get bacth inputs for forward inference computation.
-        """
-
-        input_list = []
-
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        for seq in self.sequences_set:
-            if self.is_prompts:
-                if seq.output_len > 0:
-                    input_list.append(seq.input_token_id + seq.output_token_id)
-                else:
-                    input_list.append(seq.input_token_id)
-            else:
-                input_list.append([seq.output_token_id[-1]])
-
-        max_seq_len = max(len(sub_list) for sub_list in input_list)
-
-        # We assume that all the padding_id in seq are the same at present.
-        return _make_tensor_with_pad(input_list, max_seq_len, self.sequences_set[0].pad_token_id, dtype=torch.int)
-
-    def get_1D_inputs(self) -> Tuple[torch.LongTensor, torch.Tensor]:
-        """
-        Flattening the input tokens.
-        """
-        input_list = []
-
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        for seq in self.sequences_set:
-            if self.is_prompts:
-                input_list.extend(seq.input_token_id)
-            else:
-                input_list.append(seq.output_token_id[-1])
-
-        return torch.tensor(input_list, dtype=torch.long, device=self.device)
-
-    def get_sequence_lengths(self):
-        """
-        Get the input_len of each sentence in this batch.
-        """
-        len_list = []
-
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        for seq in self.sequences_set:
-            len_list.append(seq.sentence_len)
-
-        return torch.tensor(len_list, dtype=torch.int, device=self.device)
-
-    def get_attn_mask(self) -> torch.Tensor:
-        """
-        Generate and return attention mask.
-        """
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        past_values = []
-        # We assume that all the padding_id in seq are the same at present.
-        padding_id = self.sequences_set[0].pad_token_id
-
-        for seq in self.sequences_set:
-            past_values.append(seq.input_token_id + seq.output_token_id)
-
-        max_seq_len = max(len(sub_list) for sub_list in past_values)
-        attn_mask = _make_tensor_with_pad(
-            past_values, max_seq_len, self.sequences_set[0].pad_token_id, dtype=torch.int, device=self.device
-        )
-
-        return attn_mask.ne(padding_id).long()
-
-    def __repr__(self) -> str:
-        return f"(sequences_set={self.sequences_set}, " f"is_prompts={self.is_prompts})"
-
-
 def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]:
     assert len(x) <= max_len
     return [pad] * (max_len - len(x)) + x