aphrodite-engine/aphrodite/task_handler/model_runner.py

import time
from typing import Dict, List, NamedTuple, Optional, Set, Tuple, Union

import numpy as np
import torch
import torch.nn as nn
from loguru import logger

from aphrodite.attention import AttentionMetadata, get_attn_backend
from aphrodite.common.config import (CacheConfig, DeviceConfig, LoadConfig,
                                     LoRAConfig, ModelConfig, ParallelConfig,
                                     SchedulerConfig, VisionLanguageConfig)
from aphrodite.common.sampling_params import SamplingParams
from aphrodite.common.sequence import (MultiModalData, SamplerOutput,
                                       SequenceData, SequenceGroupMetadata)
from aphrodite.common.utils import (CudaMemoryProfiler,
                                    get_kv_cache_torch_dtype, is_hip,
                                    is_pin_memory_available,
                                    make_tensor_with_pad)
from aphrodite.distributed import broadcast_tensor_dict
from aphrodite.distributed.communication_op import graph_capture
from aphrodite.distributed.parallel_state import \
    get_tensor_model_parallel_world_size
from aphrodite.lora.layers import LoRAMapping
from aphrodite.lora.request import LoRARequest
from aphrodite.lora.worker_manager import LRUCacheWorkerLoRAManager
from aphrodite.modeling import SamplingMetadata
from aphrodite.modeling.model_loader import get_model

_PAD_SLOT_ID = -1
LORA_WARMUP_RANK = 8
_BATCH_SIZE_ALIGNMENT = 8
# Capture graphs for token size 1, 2, 4, 8, 16, 24, 32, 40, ..., 256.
# NOTE: _get_graph_batch_size needs to be updated if this list is changed.
_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [
    _BATCH_SIZE_ALIGNMENT * i for i in range(1, 33)
]


class ModelInput(NamedTuple):
    input_tokens: torch.Tensor
    input_positions: torch.Tensor
    attn_metadata: Optional[AttentionMetadata]
    seq_lens: List[int]
    query_lens: List[int]
    lora_mapping: Optional[LoRAMapping]
    lora_requests: Set[LoRARequest]
    multi_modal_input: Optional[torch.Tensor]
    slot_mapping: torch.Tensor
    num_prefill_tokens: int
    num_decode_tokens: int
    num_prefills: int

    @classmethod
    def empty(cls, device):
        return ModelInput(
            input_tokens=torch.empty(0, device=device),
            input_positions=torch.empty(0, device=device),
            attn_metadata=None,
            seq_lens=[],
            query_lens=[],
            lora_mapping=None,
            lora_requests=set(),
            multi_modal_input=None,
            slot_mapping=torch.empty(0, device=device),
            num_prefill_tokens=0,
            num_decode_tokens=0,
            num_prefills=0,
        )


class PrepareDecodeMetadata(NamedTuple):
    input_tokens: List[int]
    input_positions: List[int]
    attn_metadata: Optional[AttentionMetadata]
    lora_index_mapping: List[int]
    lora_prompt_mapping: List[int]
    lora_requests: Set[LoRARequest]
    slot_mapping: List[int]

    @classmethod
    def empty(cls):
        return PrepareDecodeMetadata(
            input_tokens=[],
            input_positions=[],
            attn_metadata=None,
            lora_index_mapping=[],
            lora_prompt_mapping=[],
            lora_requests=set(),
            slot_mapping=[],
        )


class ModelRunner:

    def __init__(
        self,
        model_config: ModelConfig,
        parallel_config: ParallelConfig,
        scheduler_config: SchedulerConfig,
        device_config: DeviceConfig,
        cache_config: CacheConfig,
        load_config: LoadConfig,
        lora_config: Optional[LoRAConfig],
        kv_cache_dtype: Optional[str] = "auto",
        is_driver_worker: bool = False,
        vision_language_config: Optional[VisionLanguageConfig] = None,
    ):
        self.model_config = model_config
        self.parallel_config = parallel_config
        self.scheduler_config = scheduler_config
        self.device_config = device_config
        self.cache_config = cache_config
        self.lora_config = lora_config
        self.load_config = load_config
        self.is_driver_worker = is_driver_worker
        self.vision_language_config = vision_language_config

        self.device = self.device_config.device
        self.pin_memory = is_pin_memory_available()

        self.kv_cache_dtype = kv_cache_dtype
        self.sliding_window = model_config.get_sliding_window()
        self.block_size = cache_config.block_size
        self.max_seq_len_to_capture = self.model_config.max_seq_len_to_capture
        self.graph_runners: Dict[int, CUDAGraphRunner] = {}
        self.graph_memory_pool: Optional[Tuple[
            int, int]] = None  # Set during graph capture.
        # When using CUDA graph, the input block tables must be padded to
        # max_seq_len_to_capture. However, creating the block table in
        # Python can be expensive. To optimize this, we cache the block table
        # in numpy and only copy the actual input content at every iteration.
        # The shape of the cached block table will be
        # (max batch size to capture, max context len to capture / block size).
        self.graph_block_tables = np.zeros(
            (max(_BATCH_SIZES_TO_CAPTURE), self.get_max_block_per_batch()),
            dtype=np.int32)
        self.attn_backend = get_attn_backend(
            self.model_config.get_num_attention_heads(self.parallel_config),
            self.model_config.get_head_size(),
            self.model_config.get_num_kv_heads(self.parallel_config),
            self.model_config.get_sliding_window(),
            self.model_config.dtype,
            self.kv_cache_dtype,
            self.block_size,
        )

        # Lazy initialization
        self.model: nn.Module  # Set after load_model

        # Set if the backend is flashinfer.
        self.flashinfer_workspace_buffer: torch.Tensor
        # Set after load_model.
        self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None

    def load_model(self) -> None:
        with CudaMemoryProfiler() as m:
            # measure the time it takes to load the model
            start_time = time.time()
            self.model = get_model(
                model_config=self.model_config,
                device_config=self.device_config,
                load_config=self.load_config,
                lora_config=self.lora_config,
                vision_language_config=self.vision_language_config,
                parallel_config=self.parallel_config,
                scheduler_config=self.scheduler_config,
                cache_config=self.cache_config,
            )
            end_time = time.time()

        self.model_memory_usage = m.consumed_memory
        tp = get_tensor_model_parallel_world_size()
        total_time = end_time - start_time
        logger.info(
            f"Model weights loaded in {total_time:.2f} seconds.\nMemory usage: "
            f"{self.model_memory_usage / float(2**30):.2f} GiB x {tp} = "
            f"{self.model_memory_usage * tp / float(2**30):.2f} GiB")

        if self.lora_config:
            assert hasattr(self.model, "supported_lora_modules"
                           ) and self.model.supported_lora_modules, (
                               "Model does not support LoRA")
            assert hasattr(
                self.model,
                "embedding_modules"), "Model does not have embedding_modules"
            assert hasattr(self.model, "embedding_padding_modules"
                           ), "Model does not have embedding_padding_modules"
            self.lora_manager = LRUCacheWorkerLoRAManager(
                self.scheduler_config.max_num_seqs,
                self.scheduler_config.max_num_batched_tokens,
                self.vocab_size,
                self.lora_config,
                self.device,
                self.model.embedding_modules,
                self.model.embedding_padding_modules,
                max_position_embeddings=self.model.config.
                max_position_embeddings,
            )
            self.model = self.lora_manager.create_lora_manager(self.model)

        if self.kv_cache_dtype == "fp8" and is_hip():
            # Currently scaled KV cache is only enabled on ROCm
            if self.model_config.quantization_param_path is not None:
                if callable(getattr(self.model, "load_kv_cache_scales", None)):
                    self.model.load_kv_cache_scales(
                        self.model_config.quantization_param_path)
                else:
                    raise RuntimeError("Using FP8 KV cache and scaling factors"
                                       " provided but model "
                                       f"{self.model.__class__} does not "
                                       "support loading scaling factors.")
            else:
                logger.warning(
                    "Using FP8 KV cache but no scaling factors "
                    "provided. Defaulting to scaling factors of 1.0. "
                    "This may lead to less accurate results!")
        elif self.model_config.quantization_param_path is not None:
            logger.warning("KV cache scaling factors provided, "
                           "but the KV cache data type is not FP8. "
                           "KV cache scaling factors will not be used.")

    def save_sharded_state(
        self,
        path: str,
        pattern: Optional[str] = None,
        max_size: Optional[int] = None,
    ) -> None:
        from aphrodite.modeling.model_loader.loader import ShardedStateLoader
        ShardedStateLoader.save_model(
            self.model,
            path,
            pattern=pattern,
            max_size=max_size,
        )

    def get_max_block_per_batch(self) -> int:
        block_size = self.block_size
        return (self.max_seq_len_to_capture + block_size - 1) // block_size

    def _prepare_model_input(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
    ) -> ModelInput:
        """Prepare the model input based on a given sequence group.
        The API assumes seq_group_metadata_list is sorted by prefill -> decode.
        The result tensors and data structure also batches input in prefill
        -> decode order. For example,
        - input_tokens[:num_prefill_tokens] contains prefill tokens.
        - input_tokens[num_prefill_tokens:] contains decode tokens.
        If cuda graph is required, this API automatically pads inputs.
        """
        input_tokens: List[int] = []
        input_positions: List[int] = []
        slot_mapping: List[int] = []
        lora_index_mapping: List[int] = []
        lora_prompt_mapping: List[int] = []
        lora_requests: Set[LoRARequest] = set()

        seq_lens: List[int] = []
        prefill_seq_lens: List[int] = []
        decode_seq_lens: List[int] = []
        context_lens: List[int] = []
        query_lens: List[int] = []
        block_tables: List[List[int]] = []
        multi_modal_input_list: List[torch.Tensor] = []
        decode_only = True
        num_prefills = 0
        num_prefill_tokens = 0
        num_decode_tokens = 0

        # The following fields are only for flashinfer
        # Please follow https://docs.flashinfer.ai/tutorials/kv_layout.html#page-layout
        # for the precise definition of the following fields.
        # An example:
        # request 1, page indices [0, 5, 8]
        # request 2, page indices [1, 6, 7]
        # request 3, page indices [3, 4]
        # paged_kv_indices is a concatenation of page indices of all requests:
        # [0, 5, 8, 1, 6, 7, 3, 4]
        # paged_kv_indptr is used to index into paged_kv_indices:
        # [0, 3, 6, 8]
        paged_kv_indices: List[int] = []
        # 0 at the beginning of paged_kv_indptr indicates the start of the
        # first request’s page indices in the paged_kv_indices list.
        paged_kv_indptr: List[int] = [0]
        # paged_kv_last_page_len is the length of the last page of each request
        paged_kv_last_page_len: List[int] = []

        if len(seq_group_metadata_list) == 0:
            return ModelInput.empty(self.device)

        for seq_group_metadata in seq_group_metadata_list:
            seq_ids = list(seq_group_metadata.seq_data.keys())
            is_prompt = seq_group_metadata.is_prompt

            for seq_id in seq_ids:
                computed_block_nums = seq_group_metadata.computed_block_nums
                if (self.scheduler_config is not None
                        and self.scheduler_config.chunked_prefill_enabled
                        and not (computed_block_nums is None
                                 or computed_block_nums == [])):
                    raise RuntimeError(
                        "chunked prefill cannot be used with prefix caching "
                        "now.")

                seq_data = seq_group_metadata.seq_data[seq_id]
                if is_prompt:
                    context_len = seq_data.get_num_computed_tokens()
                else:
                    # get_num_computed_tokens is incorrect for spec decoding.
                    # So, we should have a special logic here.
                    # TODO: Fix it.
                    context_len = seq_data.get_len() - 1

                seq_len = min(
                    seq_data.get_len(),
                    context_len + seq_group_metadata.token_chunk_size)
                if is_prompt:
                    tokens = seq_data.get_token_ids()[context_len:seq_len]
                else:
                    # Optimization. get_token_ids requires the entire copy of
                    # tokens.
                    tokens = [seq_data.get_last_token_id()]

                # Prefix cache was hit.
                # Prefix is not supported with sliding_window
                prefix_cache_hit = (computed_block_nums is not None
                                    and len(computed_block_nums) > 0
                                    and self.sliding_window is None
                                    and is_prompt)

                # TODO: Combine chunked prefill and prefix caching by
                # only allowing multiple of block_size chunk size.
                # NOTE: This only works for oooooooxxx style attention.
                if prefix_cache_hit:
                    assert computed_block_nums is not None
                    context_len = len(computed_block_nums) * self.block_size
                    tokens = tokens[context_len:]
                    if self.attn_backend.get_name() == "flash-attn":
                        # NOTE: For flash-attn, the block table should
                        # include the entries for the incoming prefill tokens.
                        # TODO: This is a temporary fix. We should
                        # provide a unified interface for different backends.
                        block_table = seq_group_metadata.block_tables[seq_id]
                    else:
                        block_table = computed_block_nums
                elif (self.scheduler_config.chunked_prefill_enabled
                      or not is_prompt):
                    if seq_group_metadata.block_tables is not None:
                        # chunked prefill or decode
                        block_table = seq_group_metadata.block_tables[seq_id]
                        if self.sliding_window is not None:
                            # chunked prefill doesn't support sliding window.
                            assert (not self.scheduler_config.
                                    chunked_prefill_enabled)
                            sliding_window_blocks = (self.sliding_window //
                                                     self.block_size)
                            block_table = block_table[-sliding_window_blocks:]

                        if self.attn_backend.get_name() == "flashinfer":
                            paged_kv_indices.extend(block_table)
                            paged_kv_indptr.append(paged_kv_indptr[-1] +
                                                   len(block_table))
                            last_page_len = seq_data.get_len(
                            ) % self.block_size
                            if last_page_len == 0:
                                last_page_len = self.block_size
                            paged_kv_last_page_len.append(last_page_len)
                    else:
                        # Only happens when memory profiling runs.
                        block_table = []
                else:
                    # Prefill without chunked prefill or memory profiling.
                    block_table = []
                block_tables.append(block_table)

                # TODO: This is a hack to make sliding window work with
                # paged attn. We can remove it if we make paged attn kernel
                # to properly handle slinding window attn.
                if (self.sliding_window is not None and not is_prompt):
                    seq_len = min(seq_len, self.sliding_window)
                    context_len = seq_len - 1

                seq_lens.append(seq_len)
                context_lens.append(context_len)
                query_len = seq_len - context_len
                query_lens.append(query_len)
                input_tokens.extend(tokens)
                input_positions.extend(list(range(context_len, seq_len)))
                lora_id = seq_group_metadata.lora_int_id

                if is_prompt:
                    assert len(seq_ids) == 1
                    num_prefills += 1
                    num_prefill_tokens += len(tokens)
                    decode_only = False
                    prefill_seq_lens.append(seq_len)
                else:
                    assert query_len == 1, (
                        "seq_len: {}, context_len: {}, query_len: {}".format(
                            seq_len, context_len, query_len))
                    num_decode_tokens += query_len
                    decode_seq_lens.append(seq_len)

                if lora_id > 0:
                    lora_requests.add(seq_group_metadata.lora_request)

                lora_index_mapping += [lora_id] * (seq_len - context_len)
                lora_prompt_mapping.extend(
                    [lora_id] *
                    (seq_len -
                     context_len if seq_group_metadata.sampling_params
                     and seq_group_metadata.sampling_params.prompt_logprobs
                     else 1))

                if seq_group_metadata.multi_modal_data:
                    multi_modal_input_list.append(
                        seq_group_metadata.multi_modal_data.data)

                if _is_block_tables_empty(seq_group_metadata.block_tables):
                    # 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)
                    continue

                # Compute the slot mapping.
                block_table = seq_group_metadata.block_tables[seq_id]

                # 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].
                start_idx = 0
                if self.sliding_window is not None:
                    if is_prompt:
                        assert context_len == 0, (
                            "Prefix caching is currently not supported with "
                            "sliding window attention")
                    # It is an optimization. When it is decoding, it is always
                    # 0. When prefill, we use it to not write slots to kv cache
                    # to save memory.
                    start_idx = max(0, query_len - self.sliding_window)

                for i in range(context_len, seq_len):
                    if i < start_idx:
                        slot_mapping.append(_PAD_SLOT_ID)
                        continue

                    block_number = block_table[i // self.block_size]
                    block_offset = i % self.block_size
                    slot = block_number * self.block_size + block_offset
                    slot_mapping.append(slot)

        batch_size = len(input_tokens)
        max_query_len = max(query_lens)
        max_prefill_seq_len = max(prefill_seq_lens, default=0)
        max_decode_seq_len = max(decode_seq_lens, default=0)

        # If cuda graph can be used, pad tensors accordingly.
        # See `capture_model` API for more details.
        # vLLM uses cuda graph only for decoding requests.
        use_captured_graph = (
            decode_only and not self.model_config.enforce_eager
            and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1]
            and max_decode_seq_len <= self.max_seq_len_to_capture)
        if use_captured_graph:
            graph_batch_size = _get_graph_batch_size(batch_size)
            assert graph_batch_size >= batch_size
            for _ in range(graph_batch_size - batch_size):
                input_tokens.append(0)
                input_positions.append(0)
                slot_mapping.append(_PAD_SLOT_ID)
                seq_lens.append(1)
                block_tables.append([])
                lora_index_mapping.append(0)
            batch_size = graph_batch_size
            num_decode_tokens = batch_size

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

        context_lens_tensor = torch.tensor(context_lens,
                                           dtype=torch.int,
                                           device=self.device)

        if multi_modal_input_list:
            assert self.vision_language_config, (
                "Multi-modal inputs are only supported by "
                "vision language models.")
            multi_modal_input = torch.cat(multi_modal_input_list,
                                          dim=0).to(self.device)
        else:
            multi_modal_input = None

        seq_lens_tensor = torch.tensor(seq_lens,
                                       dtype=torch.int,
                                       device=self.device)
        query_lens_tensor = torch.tensor(query_lens,
                                         dtype=torch.long,
                                         device=self.device)
        query_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1,
                                      dtype=torch.int32,
                                      device=self.device)

        seq_lens_tensor = torch.tensor(seq_lens,
                                       dtype=torch.int,
                                       device=self.device)
        seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
                                    dtype=torch.int32,
                                    device=self.device)

        torch.cumsum(query_lens_tensor,
                     dim=0,
                     dtype=query_start_loc.dtype,
                     out=query_start_loc[1:])

        torch.cumsum(seq_lens_tensor,
                     dim=0,
                     dtype=seq_start_loc.dtype,
                     out=seq_start_loc[1:])

        input_tokens_tensor = torch.tensor(input_tokens,
                                           dtype=torch.long,
                                           device=self.device)
        input_positions_tensor = torch.tensor(input_positions,
                                              dtype=torch.long,
                                              device=self.device)
        slot_mapping_tensor = torch.tensor(slot_mapping,
                                           dtype=torch.long,
                                           device=self.device)

        if self.attn_backend.get_name() == "flashinfer":
            if not hasattr(self, "flashinfer_workspace_buffer"):
                # Allocate 16MB workspace buffer
                # Follow the example of flashinfer: https://docs.flashinfer.ai/api/python/decode.html
                self.flashinfer_workspace_buffer = torch.empty(
                    16 * 1024 * 1024, dtype=torch.uint8, device=self.device)
            paged_kv_indptr_tensor = torch.tensor(paged_kv_indptr,
                                                  dtype=torch.int,
                                                  device=self.device)
            paged_kv_indices_tensor = torch.tensor(paged_kv_indices,
                                                   dtype=torch.int,
                                                   device=self.device)
            paged_kv_last_page_len_tensor = torch.tensor(
                paged_kv_last_page_len, dtype=torch.int, device=self.device)
            kv_cache_dtype = get_kv_cache_torch_dtype(self.kv_cache_dtype,
                                                      self.model_config.dtype)
            attn_metadata = self.attn_backend.make_metadata(
                num_prefills=num_prefills,
                slot_mapping=slot_mapping_tensor,
                num_prefill_tokens=num_prefill_tokens,
                num_decode_tokens=num_decode_tokens,
                use_cuda_graph=False,
                max_prefill_seq_len=max_prefill_seq_len,
                block_tables=block_tables,
                workspace_buffer=self.flashinfer_workspace_buffer,
                paged_kv_indptr=paged_kv_indptr_tensor,
                paged_kv_indices=paged_kv_indices_tensor,
                paged_kv_last_page_len=paged_kv_last_page_len_tensor,
                num_qo_heads=self.model_config.get_num_attention_heads(
                    self.parallel_config),
                num_kv_heads=self.model_config.get_num_kv_heads(
                    self.parallel_config),
                head_dim=self.model_config.get_head_size(),
                page_size=16,
                seq_start_loc=seq_start_loc,
                data_type=kv_cache_dtype)
        else:
            attn_metadata = self.attn_backend.make_metadata(
                num_prefills=num_prefills,
                slot_mapping=slot_mapping_tensor,
                num_prefill_tokens=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,
            )

        if self.lora_config:
            lora_mapping = LoRAMapping(
                lora_index_mapping,
                lora_prompt_mapping,
            )
        else:
            lora_mapping = None

        return ModelInput(
            input_tokens=input_tokens_tensor,
            input_positions=input_positions_tensor,
            attn_metadata=attn_metadata,
            seq_lens=seq_lens,
            query_lens=query_lens,
            lora_mapping=lora_mapping,
            lora_requests=lora_requests,
            multi_modal_input=multi_modal_input,
            slot_mapping=slot_mapping_tensor,
            num_prefill_tokens=num_prefill_tokens,
            num_decode_tokens=num_decode_tokens,
            num_prefills=num_prefills,
        )

    def prepare_input_tensors(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata,
               Set[LoRARequest], LoRAMapping, torch.Tensor]:
        if self.is_driver_worker:
            # Prepare input tensors.
            (
                input_tokens,
                input_positions,
                attn_metadata,
                seq_lens,
                query_lens,
                lora_mapping,
                lora_requests,
                multi_modal_input,
                slot_mapping,
                num_prefill_tokens,
                num_decode_tokens,
                num_prefills,
            ) = self._prepare_model_input(seq_group_metadata_list)
            sampling_metadata = SamplingMetadata.prepare(
                seq_group_metadata_list, seq_lens, query_lens, self.device,
                self.pin_memory)

            metadata_dict = {
                "input_tokens": input_tokens,
                "input_positions": input_positions,
                "selected_token_indices":
                sampling_metadata.selected_token_indices,
                "lora_requests": lora_requests,
                "lora_mapping": lora_mapping,
                "multi_modal_input": multi_modal_input,
                "num_prefill_tokens": num_prefill_tokens,
                "num_decode_tokens": num_decode_tokens,
                "slot_mapping": slot_mapping,
                "num_prefills": num_prefills,
            }
            if attn_metadata:
                metadata_dict.update(attn_metadata.asdict_zerocopy())
            broadcast_tensor_dict(metadata_dict, src=0)
        else:
            metadata_dict = broadcast_tensor_dict(src=0)
            input_tokens = metadata_dict.pop("input_tokens")
            input_positions = metadata_dict.pop("input_positions")
            selected_token_indices = metadata_dict.pop(
                "selected_token_indices")
            lora_mapping = metadata_dict.pop("lora_mapping")
            lora_requests = metadata_dict.pop("lora_requests")
            multi_modal_input = metadata_dict.pop("multi_modal_input")
            if metadata_dict:
                attn_metadata = self.attn_backend.make_metadata(
                    **metadata_dict)
            else:
                attn_metadata = None
            sampling_metadata = SamplingMetadata(
                seq_groups=None,
                selected_token_indices=selected_token_indices,
                categorized_sample_indices=None,
                num_prompts=0,
            )

        return (input_tokens, input_positions, attn_metadata,
                sampling_metadata, lora_requests, lora_mapping,
                multi_modal_input)

    @torch.inference_mode()
    def execute_model(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        kv_caches: List[torch.Tensor],
    ) -> Optional[SamplerOutput]:
        (input_tokens, input_positions, attn_metadata, sampling_metadata,
         lora_requests, lora_mapping, multi_modal_input
         ) = self.prepare_input_tensors(seq_group_metadata_list)

        if self.lora_config:
            self.set_active_loras(lora_requests, lora_mapping)

        # Currently cuda graph is only supported by the decode phase.
        prefill_meta = attn_metadata.prefill_metadata
        decode_meta = attn_metadata.decode_metadata
        if prefill_meta is None and decode_meta.use_cuda_graph:
            graph_batch_size = input_tokens.shape[0]
            model_executable = self.graph_runners[graph_batch_size]
        else:
            model_executable = self.model
        execute_model_kwargs = {
            "input_ids": input_tokens,
            "positions": input_positions,
            "kv_caches": kv_caches,
            "attn_metadata": attn_metadata,
        }
        if self.vision_language_config:
            execute_model_kwargs.update({"image_input": multi_modal_input})
        hidden_states = model_executable(**execute_model_kwargs)

        # Compute the logits.
        logits = self.model.compute_logits(hidden_states, sampling_metadata)

        # Only perform sampling in the driver worker.
        if not self.is_driver_worker:
            return None

        # Sample the next token.
        output = self.model.sample(
            logits=logits,
            sampling_metadata=sampling_metadata,
        )

        return output

    @torch.inference_mode()
    def profile_run(self) -> None:
        # Enable top-k sampling to reflect the accurate memory usage.
        sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
        max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
        max_num_seqs = self.scheduler_config.max_num_seqs

        # This represents the maximum number of different requests
        # that will have unique loras, an therefore the max amount of memory
        # consumption create dummy lora request copies from the lora request
        # passed in, which contains a lora from the lora warmup path.
        dummy_lora_requests = []
        dummy_lora_requests_per_seq = []
        if self.lora_config:
            assert self.lora_manager is not None
            with self.lora_manager.dummy_lora_cache():
                for idx in range(self.lora_config.max_loras):
                    lora_id = idx + 1
                    dummy_lora_request = LoRARequest(
                        lora_name=f"warmup_{lora_id}",
                        lora_int_id=lora_id,
                        lora_local_path="/not/a/real/path",
                    )
                    self.lora_manager.add_dummy_lora(dummy_lora_request,
                                                     rank=LORA_WARMUP_RANK)
                    dummy_lora_requests.append(dummy_lora_request)
                dummy_lora_requests_per_seq = [
                    dummy_lora_requests[idx % len(dummy_lora_requests)]
                    for idx in range(max_num_seqs)
                ]

        # Profile memory usage with max_num_sequences sequences and the total
        # number of tokens equal to max_num_batched_tokens.
        seqs: List[SequenceGroupMetadata] = []
        # Additional GPU memory may be needed for vision encoding, which needs
        # to be accounted for when calculating the GPU blocks for
        # Aphrodite blocker manager.
        # To exercise the worst scenario for GPU memory consumption,
        # the number of seqs (batch_size) is chosen to maximize the number
        # of images processed.
        if self.vision_language_config:
            max_num_seqs = min(
                max_num_seqs,
                int(max_num_batched_tokens /
                    self.vision_language_config.image_feature_size))
        for group_id in range(max_num_seqs):
            seq_len = (max_num_batched_tokens // max_num_seqs +
                       (group_id < max_num_batched_tokens % max_num_seqs))
            seq_data, fake_multi_modal_input = _prepare_fake_inputs(
                seq_len, self.vision_language_config)
            seq = SequenceGroupMetadata(
                request_id=str(group_id),
                is_prompt=True,
                seq_data={group_id: seq_data},
                sampling_params=sampling_params,
                block_tables=None,
                lora_request=dummy_lora_requests_per_seq[group_id]
                if dummy_lora_requests_per_seq else None,
                multi_modal_data=fake_multi_modal_input,
            )
            seqs.append(seq)

        # Run the model with the dummy inputs.
        num_layers = self.model_config.get_num_layers(self.parallel_config)
        kv_caches = [None] * num_layers
        self.execute_model(seqs, kv_caches)
        torch.cuda.synchronize()
        return

    def remove_all_loras(self):
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
        self.lora_manager.remove_all_loras()

    def set_active_loras(self, lora_requests: Set[LoRARequest],
                         lora_mapping: LoRAMapping) -> None:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
        self.lora_manager.set_active_loras(lora_requests, lora_mapping)

    def add_lora(self, lora_request: LoRARequest) -> bool:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
        return self.lora_manager.add_lora(lora_request)

    def remove_lora(self, lora_id: int) -> bool:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
        return self.lora_manager.remove_lora(lora_id)

    def list_loras(self) -> Set[int]:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
        return self.lora_manager.list_loras()

    @torch.inference_mode()
    def capture_model(self, kv_caches: List[torch.Tensor]) -> None:
        """Cuda graph capture a model.

        Note that CUDA graph's performance gain is negligible if number
        of batched tokens are larger than 200. And since CUDA graph
        requires fixed sized tensors, supporting large/variable batch
        size requires high GPU memory overhead. Thus, vLLM only captures
        decoding requests. Mixed batch (chunked prefill + decoding) or
        prefill requests are not captured.

        Since it is used for decoding-only, it assumes there's only 1 token
        per sequence in the batch.
        """

        assert not self.model_config.enforce_eager
        logger.info("Capturing the model for CUDA graphs. This may lead to "
                    "unexpected consequences if the model is not static. To "
                    "run the model in eager mode, set 'enforce_eager=True' or "
                    "use '--enforce-eager' in the CLI.")
        logger.info("CUDA graphs can take additional 1~3 GiB memory per GPU. "
                    "If you are running out of memory, consider decreasing "
                    "`gpu_memory_utilization` or enforcing eager mode. "
                    "You can also reduce the `max_num_seqs` as needed "
                    "to decrease memory usage.")
        start_time = time.perf_counter()

        # Prepare dummy inputs. These will be reused for all batch sizes.
        max_batch_size = max(_BATCH_SIZES_TO_CAPTURE)
        input_tokens = torch.zeros(max_batch_size, dtype=torch.long).cuda()
        input_positions = torch.zeros(max_batch_size, dtype=torch.long).cuda()
        slot_mapping = torch.empty(max_batch_size, dtype=torch.long).cuda()
        slot_mapping.fill_(_PAD_SLOT_ID)
        seq_lens = torch.ones(max_batch_size, dtype=torch.int32).cuda()
        block_tables = torch.from_numpy(self.graph_block_tables).cuda()

        graph_batch_size = _get_graph_batch_size(
            self.scheduler_config.max_num_seqs)
        batch_size_capture_list = [
            bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
        ]

        with graph_capture() as graph_capture_context:
            # NOTE: Capturing the largest batch size first may help reduce the
            # memory usage of CUDA graph.
            for batch_size in reversed(batch_size_capture_list):
                # Create dummy attn_metadata.
                attn_metadata = self.attn_backend.make_metadata(
                    num_prefills=0,
                    num_prefill_tokens=0,
                    num_decode_tokens=batch_size,
                    slot_mapping=slot_mapping[:batch_size],
                    seq_lens=None,
                    seq_lens_tensor=seq_lens[:batch_size],
                    max_query_len=None,
                    max_prefill_seq_len=0,
                    max_decode_seq_len=self.max_seq_len_to_capture,
                    query_start_loc=None,
                    seq_start_loc=None,
                    context_lens_tensor=None,
                    block_tables=block_tables[:batch_size],
                    use_cuda_graph=True,
                )

                if self.lora_config:
                    lora_mapping = LoRAMapping(
                        [0] * batch_size,
                        [0] * batch_size,
                    )
                    self.set_active_loras(set(), lora_mapping)

                graph_runner = CUDAGraphRunner(self.model)
                graph_runner.capture(
                    input_tokens[:batch_size],
                    input_positions[:batch_size],
                    kv_caches,
                    attn_metadata,
                    memory_pool=self.graph_memory_pool,
                    stream=graph_capture_context.stream,
                )
                self.graph_memory_pool = graph_runner.graph.pool()
                self.graph_runners[batch_size] = graph_runner

        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        # This usually takes < 10 seconds.
        logger.info(f"Graph capturing finished in {elapsed_time} secs.")

    def __del__(self) -> None:
        # Delete the CUDA graphs before deleting the pynccl communicator.
        # NOTE: This is necessary because otherwise deadlocks can
        # happen.
        # FIXME: This is a bit hacky. Find a more robust solution.
        # TODO: when we get enough user feedback that pynccl is
        # more stable than cupy, we can remove this, e.g. in v0.4.1.
        self.graph_runners.clear()
        self.pynccl_backend = None

    @property
    def vocab_size(self) -> int:
        return self.model_config.get_vocab_size()


class CUDAGraphRunner:

    def __init__(self, model: nn.Module):
        self.model = model
        self.input_buffers: Dict[str, torch.Tensor] = {}
        self.output_buffers: Dict[str, torch.Tensor] = {}

        self._graph: Optional[torch.cuda.CUDAGraph] = None

    @property
    def graph(self):
        assert self._graph is not None
        return self._graph

    def capture(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        memory_pool: Optional[Tuple[int, int]],
        stream: torch.cuda.Stream,
        **kwargs,
    ) -> None:
        assert self._graph is None
        # Run the model once without capturing the graph.
        # This is to make sure that the captured graph does not include the
        # kernel launches for initial benchmarking (e.g., Triton autotune).
        self.model(
            input_ids,
            positions,
            kv_caches,
            attn_metadata,
            **kwargs,
        )
        torch.cuda.synchronize()

        # Capture the graph.
        self._graph = torch.cuda.CUDAGraph()
        with torch.cuda.graph(self._graph, pool=memory_pool, stream=stream):
            hidden_states = self.model(
                input_ids,
                positions,
                kv_caches,
                attn_metadata,
                **kwargs,
            )
        torch.cuda.synchronize()

        # Save the input and output buffers.
        self.input_buffers = {
            "input_ids": input_ids,
            "positions": positions,
            "kv_caches": kv_caches,
            "slot_mapping": attn_metadata.slot_mapping,
            "seq_lens_tensor": attn_metadata.decode_metadata.seq_lens_tensor,
            "block_tables": attn_metadata.decode_metadata.block_tables,
        }
        self.output_buffers = {"hidden_states": hidden_states}
        return

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        **kwargs,
    ) -> torch.Tensor:
        # KV caches are fixed tensors, so we don't need to copy them.
        del kv_caches

        # Copy the input tensors to the input buffers.
        self.input_buffers["input_ids"].copy_(input_ids, non_blocking=True)
        self.input_buffers["positions"].copy_(positions, non_blocking=True)
        self.input_buffers["slot_mapping"].copy_(attn_metadata.slot_mapping,
                                                 non_blocking=True)
        self.input_buffers["seq_lens_tensor"].copy_(
            attn_metadata.decode_metadata.seq_lens_tensor, non_blocking=True)
        self.input_buffers["block_tables"].copy_(
            attn_metadata.decode_metadata.block_tables, non_blocking=True)
        # Run the graph.
        self.graph.replay()

        # Return the output tensor.
        return self.output_buffers["hidden_states"]

    def __call__(self, *args, **kwargs):
        return self.forward(*args, **kwargs)


def _get_graph_batch_size(batch_size: int) -> int:
    """Returns the padded batch size given actual batch size.

    Batch sizes are 1, 2, 4, _BATCH_SIZE_ALIGNMENT,
    2*_BATCH_SIZE_ALIGNMENT, 3*_BATCH_SIZE_ALIGNMENT...
    """
    if batch_size <= 2:
        return batch_size
    elif batch_size <= 4:
        return 4
    else:
        return ((batch_size + _BATCH_SIZE_ALIGNMENT - 1) //
                _BATCH_SIZE_ALIGNMENT * _BATCH_SIZE_ALIGNMENT)


def _prepare_fake_inputs(
        seq_len: int, vision_language_config: Optional[VisionLanguageConfig]):
    """Prepare fake inputs for profile run."""
    if vision_language_config:
        prompt_tokens = [
            vision_language_config.image_token_id
        ] * vision_language_config.image_feature_size + [0] * (
            seq_len - vision_language_config.image_feature_size)
        fake_image_input = MultiModalData(
            type=MultiModalData.Type.IMAGE,
            data=torch.zeros(vision_language_config.image_input_shape,
                             dtype=torch.float16))
    else:
        prompt_tokens = [0] * seq_len
        fake_image_input = None
    return SequenceData(prompt_tokens), fake_image_input


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