aphrodite-engine/aphrodite/attention/backends/utils.py

from typing import TYPE_CHECKING, Dict, List, Type, TypeVar, Union

import numpy as np
import torch

from aphrodite.attention import AttentionMetadata, AttentionMetadataBuilder
from aphrodite.common.utils import async_tensor_h2d, make_tensor_with_pad

# Error string(s) for encoder/decoder
# unsupported attention scenarios
STR_NOT_IMPL_ENC_DEC_ROCM_HIP = ("ROCm/HIP is not currently supported "
                                 "with encoder/decoder models.")

PAD_SLOT_ID = -1

# Switch to numpy implementation of compute_slot_mapping
# if we have at least this many elements. Could be tuned further.
_COMPUTE_SLOT_MAPPING_NUMPY_NUMEL = 256

if TYPE_CHECKING:
    from aphrodite.task_handler.model_runner import ModelInputForGPUBuilder


def is_block_tables_empty(block_tables: Union[None, Dict]):
    """
    Check if block_tables is None or a dictionary with all None values.
    """
    if block_tables is None:
        return True
    if isinstance(block_tables, dict) and all(
            value is None for value in block_tables.values()):
        return True
    return False


def compute_slot_mapping_start_idx(is_prompt: bool, query_len: int,
                                   context_len: int, sliding_window: int,
                                   use_v2_block_manager: bool):
    """
    Compute the start index of slot mapping.
    """
    start_idx = 0
    if is_prompt and sliding_window is not None:
        assert use_v2_block_manager or context_len == 0, (
            "Prefix caching is currently not supported with "
            "sliding window attention in V1 block manager")
        # When prefill, we use it to not write slots to kv cache
        # to save memory.
        start_idx = max(0, query_len - sliding_window)
    return start_idx


def _compute_slot_mapping_python(slot_mapping: List[int],
                                 block_table: List[int], range_start: int,
                                 range_end: int, block_size: int):
    for i in range(range_start, range_end):
        block_number = block_table[i // block_size]
        block_offset = i % block_size
        slot = block_number * block_size + block_offset
        slot_mapping.append(slot)


def _compute_slot_mapping_numpy(slot_mapping: List[int],
                                block_table: List[int], range_start: int,
                                range_end: int, block_size: int):
    block_table_array = np.array(block_table)
    idx = np.arange(range_start, range_end)
    block_offset = idx % block_size
    idx //= block_size
    seq_slot_mapping_array = block_table_array[idx]
    seq_slot_mapping_array *= block_size
    seq_slot_mapping_array += block_offset
    slot_mapping.extend(seq_slot_mapping_array)


def compute_slot_mapping(is_profile_run: bool, slot_mapping: List[int],
                         seq_id: int, seq_len: int, context_len: int,
                         start_idx: int, block_size: int,
                         block_tables: Dict[int, List[int]]):
    """
    Compute slot mapping.
    """
    if is_profile_run:
        # During memory profiling, the block tables are not
        # initialized yet. In this case, we just use a dummy
        # slot mapping.
        # In embeddings, the block tables are {seq_id: None}.
        slot_mapping.extend([PAD_SLOT_ID] * seq_len)
        return

    # Mask the [0, start_idx) tokens of the prompt with
    # PAD_SLOT_ID, where start_idx is max(0, seq_len -
    # sliding_window). For example, if the prompt len is 10,
    # sliding window is 8, and block size is 4, the first two
    # tokens are masked and the slot mapping will be
    # [-1, -1, 2, 3, 4, 5, 6, 7, 0, 1].
    padding_mask_len = max(0, start_idx - context_len)
    slot_mapping.extend([PAD_SLOT_ID] * padding_mask_len)

    range_start = max(start_idx, context_len)
    range_end = seq_len
    numel = range_end - range_start
    block_table = block_tables[seq_id]

    # numpy implementation will be faster than python if we have
    # many elements, otherwise it will be slower.
    if numel < _COMPUTE_SLOT_MAPPING_NUMPY_NUMEL:
        _compute_slot_mapping_python(slot_mapping, block_table, range_start,
                                     range_end, block_size)
    else:
        _compute_slot_mapping_numpy(slot_mapping, block_table, range_start,
                                    range_end, block_size)


TAttentionMetadata = TypeVar("TAttentionMetadata", bound='AttentionMetadata')


class CommonMetadataBuilder(AttentionMetadataBuilder[TAttentionMetadata]):

    _metadata_cls: Type[TAttentionMetadata]

    def __init__(self, input_builder: "ModelInputForGPUBuilder"):
        self.slot_mapping: List[int] = []
        self.prefill_seq_lens: List[int] = []
        self.context_lens: List[int] = []
        self.block_tables: List[List[int]] = []
        self.curr_seq_lens: List[int] = []
        self.num_prefills = 0
        self.num_prefill_tokens = 0
        self.num_decode_tokens = 0

        self.input_builder = input_builder
        self.runner = input_builder.runner

        self.sliding_window = input_builder.sliding_window
        self.block_size = input_builder.block_size
        self.use_v2_block_manager = (
            input_builder.scheduler_config.use_v2_block_manager)

    def _add_seq_group(
            self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
            chunked_prefill_enabled: bool):
        is_prompt = inter_data.is_prompt
        block_tables = inter_data.block_tables
        computed_block_nums = inter_data.computed_block_nums

        for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
             curr_sliding_window_block) in zip(
                 inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
                 inter_data.orig_seq_lens, inter_data.seq_lens,
                 inter_data.query_lens, inter_data.context_lens,
                 inter_data.curr_sliding_window_blocks):
            self.context_lens.append(context_len)
            if is_prompt:
                self.num_prefills += 1
                self.num_prefill_tokens += token_len
                self.prefill_seq_lens.append(seq_len)
            else:
                assert query_len == 1, (
                    "seq_len: {}, context_len: {}, query_len: {}".format(
                        seq_len, context_len, query_len))
                self.num_decode_tokens += query_len
                self.curr_seq_lens.append(curr_seq_len)

            # Compute block table.
            # TODO: Combine chunked prefill and prefix caching by
            # only allowing multiple of block_size chunk size.
            # NOTE: This only works for oooooooxxx style attention.
            block_table = []
            if inter_data.prefix_cache_hit:
                block_table = computed_block_nums
            elif ((chunked_prefill_enabled or not is_prompt)
                  and block_tables is not None):
                block_table = block_tables[seq_id][-curr_sliding_window_block:]
            self.block_tables.append(block_table)

            # Compute slot mapping.
            is_profile_run = is_block_tables_empty(block_tables)
            start_idx = compute_slot_mapping_start_idx(
                is_prompt, query_len, context_len, self.sliding_window,
                self.use_v2_block_manager)
            compute_slot_mapping(is_profile_run, self.slot_mapping, seq_id,
                                 seq_len, context_len, start_idx,
                                 self.block_size, inter_data.block_tables)

    def build(self, seq_lens: List[int], query_lens: List[int],
              cuda_graph_pad_size: int, batch_size: int):
        """Build attention metadata with on-device tensors.
        Args:
            seq_lens: The maybe padded sequence lengths of the input sequences.
            query_lens: The query lengths of the input sequences.
            cuda_graph_pad_size: The padding size for cuda graph.
                                 -1 if cuda graph is not used.
            batch_size: The maybe padded batch size.
        """
        for inter_data in self.input_builder.inter_data_list:
            self._add_seq_group(inter_data,
                                self.input_builder.chunked_prefill_enabled)

        device = self.runner.device
        use_captured_graph = cuda_graph_pad_size != -1

        max_query_len = max(query_lens)
        max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
        max_decode_seq_len = max(self.curr_seq_lens, default=0)
        num_decode_tokens = self.num_decode_tokens

        if use_captured_graph:
            self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
            self.block_tables.extend([] * cuda_graph_pad_size)
            num_decode_tokens = batch_size

            # The shape of graph_block_tables is
            # [max batch size, max context len // block size].
            input_block_tables = self.runner.graph_block_tables[:batch_size]
            for i, block_table in enumerate(self.block_tables):
                if block_table:
                    input_block_tables[i, :len(block_table)] = block_table
            block_tables = torch.from_numpy(input_block_tables).to(
                device, non_blocking=True)
        else:
            block_tables = make_tensor_with_pad(
                self.block_tables,
                pad=0,
                dtype=torch.int,
                device=device,
            )
        assert max_query_len > 0, "query_lens: {}".format(query_lens)

        assert device is not None
        context_lens_tensor = async_tensor_h2d(self.context_lens, torch.int,
                                               device, self.runner.pin_memory)
        seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
                                           self.runner.pin_memory)
        query_lens_tensor = async_tensor_h2d(query_lens, torch.long, device,
                                             self.runner.pin_memory)
        slot_mapping_tensor = async_tensor_h2d(self.slot_mapping, torch.long,
                                               device, self.runner.pin_memory)
        query_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1,
                                      dtype=torch.int32,
                                      device=device)
        seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
                                    dtype=torch.int32,
                                    device=device)
        torch.cumsum(seq_lens_tensor,
                     dim=0,
                     dtype=seq_start_loc.dtype,
                     out=seq_start_loc[1:])
        torch.cumsum(query_lens_tensor,
                     dim=0,
                     dtype=query_start_loc.dtype,
                     out=query_start_loc[1:])

        return self._metadata_cls(  # type: ignore
            num_prefills=self.num_prefills,
            slot_mapping=slot_mapping_tensor,
            num_prefill_tokens=self.num_prefill_tokens,
            num_decode_tokens=num_decode_tokens,
            seq_lens=seq_lens,
            seq_lens_tensor=seq_lens_tensor,
            max_query_len=max_query_len,
            max_prefill_seq_len=max_prefill_seq_len,
            max_decode_seq_len=max_decode_seq_len,
            query_start_loc=query_start_loc,
            seq_start_loc=seq_start_loc,
            context_lens_tensor=context_lens_tensor,
            block_tables=block_tables,
            use_cuda_graph=use_captured_graph,
        )