import contextlib
import time
from typing import Dict, List, Optional, Set, Tuple

import numpy as np
import torch
import torch.nn as nn

from vllm.attention import AttentionMetadata, get_attn_backend
from vllm.config import (DeviceConfig, LoRAConfig, ModelConfig, ParallelConfig,
                         SchedulerConfig, VisionLanguageConfig)
from vllm.logger import init_logger
from vllm.lora.layers import LoRAMapping
from vllm.lora.request import LoRARequest
from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
from vllm.model_executor import SamplingMetadata
from vllm.model_executor.model_loader import get_model
from vllm.model_executor.parallel_utils import custom_all_reduce, pynccl_utils
from vllm.model_executor.parallel_utils.communication_op import (
    broadcast_tensor_dict)
from vllm.model_executor.parallel_utils.parallel_state import (
    with_pynccl_for_all_reduce)
from vllm.sampling_params import SamplingParams, SamplingType
from vllm.sequence import (MultiModalData, SamplerOutput, SequenceData,
                           SequenceGroupMetadata)
from vllm.utils import (CudaMemoryProfiler, async_tensor_h2d, is_hip,
                        is_pin_memory_available, make_tensor_with_pad,
                        maybe_expand_dim)

logger = init_logger(__name__)

_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 ModelRunner:

    def __init__(
        self,
        model_config: ModelConfig,
        parallel_config: ParallelConfig,
        scheduler_config: SchedulerConfig,
        device_config: DeviceConfig,
        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.lora_config = lora_config
        self.is_driver_worker = is_driver_worker

        # model_config can be None in tests/samplers/test_sampler.py.
        # FIXME(woosuk): This is a hack to make the tests work. Refactor this.
        self.sliding_window = (model_config.get_sliding_window()
                               if model_config is not None else None)
        self.device_config = (device_config
                              if device_config is not None else DeviceConfig())
        self.device = self.device_config.device

        self.model = None
        self.block_size = None  # Set after initial profiling.
        self.lora_manager = None

        self.graph_runners: Dict[int, CUDAGraphRunner] = {}
        self.graph_memory_pool = None  # Set during graph capture.

        self.max_context_len_to_capture = (
            self.model_config.max_context_len_to_capture
            if self.model_config is not None else 0)
        # When using CUDA graph, the input block tables must be padded to
        # max_context_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 = None  # Set after initial profiling.
        self.pin_memory = is_pin_memory_available()
        self.kv_cache_dtype = kv_cache_dtype
        self.vision_language_config = vision_language_config

        self.attn_backend = get_attn_backend(
            self.model_config.dtype if model_config is not None else None)

    def load_model(self) -> None:
        with CudaMemoryProfiler() as m:
            self.model = get_model(
                self.model_config,
                self.device_config,
                lora_config=self.lora_config,
                vision_language_config=self.vision_language_config,
                parallel_config=self.parallel_config,
                scheduler_config=self.scheduler_config)

        self.model_memory_usage = m.consumed_memory
        logger.info(f"Loading model weights took "
                    f"{self.model_memory_usage / float(2**30):.4f} GB")

        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)
            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.warn("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.warn("KV cache scaling factors provided, "
                        "but the KV cache data type is not FP8. "
                        "KV cache scaling factors will not be used.")

    def set_block_size(self, block_size: int) -> None:
        self.block_size = block_size

        self.graph_block_tables = np.zeros(
            (max(_BATCH_SIZES_TO_CAPTURE), self.get_max_block_per_batch()),
            dtype=np.int32)

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

    def _prepare_prompt(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, List[int],
               List[int], List[int], List[int], Set[LoRARequest],
               torch.Tensor]:
        assert len(seq_group_metadata_list) > 0
        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()

        prompt_lens: List[int] = []
        context_lens: List[int] = []
        subquery_lens: List[int] = []
        prefix_block_tables: List[List[int]] = []
        multi_modal_input_list: List[torch.Tensor] = []

        for seq_group_metadata in seq_group_metadata_list:
            assert seq_group_metadata.is_prompt
            seq_ids = list(seq_group_metadata.seq_data.keys())
            assert len(seq_ids) == 1
            seq_id = seq_ids[0]

            computed_block_nums = seq_group_metadata.computed_block_nums
            if (self.scheduler_config is not None
                    and self.scheduler_config.chunked_prefill_enabled
                    and computed_block_nums is not None):
                raise RuntimeError(
                    "chunked prefill cannot be used with prefix caching "
                    "now.")

            token_chunk_size = seq_group_metadata.token_chunk_size
            seq_data = seq_group_metadata.seq_data[seq_id]
            computed_len = seq_data.get_num_computed_tokens()
            # We should use get_len here because in case of preemption
            # it contains output tokens.
            prefill_end = min(seq_data.get_len(),
                              computed_len + token_chunk_size)
            # TODO(sang): Rename it after chunked prefill is introduced.
            prompt_tokens = seq_data.get_token_ids()[computed_len:prefill_end]
            prompt_len = len(prompt_tokens)
            # Right now, the prefill_end is always same as the length of
            # sequence. However, once chunked prefill is introduced, this
            # assumption can be changed.
            assert prefill_end == seq_data.get_len()
            prompt_lens.append(prompt_len)

            # NOTE: This only works for oooooooxxx style attention.
            if computed_block_nums is not None and len(
                    computed_block_nums) > 0 and self.sliding_window is None:
                # Prefix is not supported with sliding_window
                computed_len = len(computed_block_nums) * self.block_size
                prompt_tokens = prompt_tokens[computed_len:]
                prefix_block_tables.append(computed_block_nums)
            else:
                prefix_block_tables.append([])
                # Right now, prefill start is always 0. However, this
                # assumption can be changed once chunked prefill is introduced.
                assert computed_len == 0

            # actual prompt lens
            context_lens.append(computed_len)
            subquery_lens.append(prompt_len - computed_len)

            input_tokens.extend(prompt_tokens)
            # NOTE(woosuk): Here we assume that the first token in the prompt
            # is always the first token in the sequence.
            input_positions.extend(list(range(computed_len, prefill_end)))
            lora_id = seq_group_metadata.lora_int_id

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

            lora_index_mapping += [lora_id] * (prompt_len - computed_len)
            lora_prompt_mapping.extend(
                [lora_id] *
                (prompt_len - computed_len
                 if 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 seq_group_metadata.block_tables is None:
                # During memory profiling, the block tables are not initialized
                # yet. In this case, we just use a dummy slot mapping.
                slot_mapping.extend([_PAD_SLOT_ID] * prompt_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, prompt_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:
                assert computed_len == 0, (
                    "Prefix caching is currently not supported with "
                    "sliding window attention")
                start_idx = max(0, prompt_len - self.sliding_window)

            for i in range(computed_len, prefill_end):
                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)

        max_subquery_len = max(subquery_lens)
        max_prompt_len = max(prompt_lens)
        num_prompt_tokens = len(input_tokens)
        assert max_subquery_len > 0

        input_tokens = torch.tensor(input_tokens,
                                    dtype=torch.long,
                                    device=self.device)
        input_positions = torch.tensor(input_positions,
                                       dtype=torch.long,
                                       device=self.device)
        slot_mapping = torch.tensor(slot_mapping,
                                    dtype=torch.long,
                                    device=self.device)
        lora_index_mapping = lora_index_mapping

        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

        # Prepare prefix block tables
        max_prompt_block_table_len = max(len(t) for t in prefix_block_tables)
        block_tables = make_tensor_with_pad(
            prefix_block_tables,
            max_len=max_prompt_block_table_len,
            pad=0,
            dtype=torch.int,
            device=self.device,
        )

        # Query length can be shorter than key (i.e., prompt) when prefill
        # is chunked or prefix cached.
        subquery_lens_tensor = torch.tensor(subquery_lens,
                                            dtype=torch.long,
                                            device=self.device)
        subquery_start_loc = torch.zeros(subquery_lens_tensor.shape[0] + 1,
                                         dtype=torch.int32,
                                         device=self.device)

        prompt_lens_tensor = torch.tensor(prompt_lens,
                                          dtype=torch.long,
                                          device=self.device)
        seq_start_loc = torch.zeros(prompt_lens_tensor.shape[0] + 1,
                                    dtype=torch.int32,
                                    device=self.device)

        torch.cumsum(subquery_lens_tensor,
                     dim=0,
                     dtype=subquery_start_loc.dtype,
                     out=subquery_start_loc[1:])

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

        attn_metadata = self.attn_backend.make_metadata(
            is_prompt=True,
            slot_mapping=slot_mapping,
            prompt_lens=prompt_lens,
            prompt_lens_tensor=prompt_lens_tensor,
            num_prompt_tokens=num_prompt_tokens,
            num_generation_tokens=0,
            max_subquery_len=max_subquery_len,
            max_context_len=None,
            max_prompt_len=max_prompt_len,
            subquery_start_loc=subquery_start_loc,
            seq_start_loc=seq_start_loc,
            context_lens=context_lens_tensor,
            block_tables=block_tables,
            use_cuda_graph=False,
            kv_cache_dtype=self.kv_cache_dtype,
        )
        return (input_tokens, input_positions, attn_metadata, prompt_lens,
                subquery_lens, lora_index_mapping, lora_prompt_mapping,
                lora_requests, multi_modal_input)

    def _prepare_decode(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, List[int],
               List[int], Set[LoRARequest]]:
        assert len(seq_group_metadata_list) > 0
        input_tokens: List[int] = []
        input_positions: List[int] = []
        slot_mapping: List[int] = []
        context_lens: List[int] = []
        block_tables: List[List[int]] = []
        lora_index_mapping: List[int] = []
        lora_prompt_mapping: List[int] = []
        lora_requests: Set[LoRARequest] = set()

        for seq_group_metadata in seq_group_metadata_list:
            assert not seq_group_metadata.is_prompt
            assert seq_group_metadata.token_chunk_size == 1

            seq_ids = list(seq_group_metadata.seq_data.keys())
            lora_id = seq_group_metadata.lora_int_id

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

            for seq_id in seq_ids:
                seq_data = seq_group_metadata.seq_data[seq_id]
                generation_token = seq_data.get_last_token_id()
                input_tokens.append(generation_token)

                seq_len = seq_data.get_len()
                position = seq_len - 1
                input_positions.append(position)

                context_len = seq_len if self.sliding_window is None else min(
                    seq_len, self.sliding_window)
                context_lens.append(context_len)

                block_table = seq_group_metadata.block_tables[seq_id]
                block_number = block_table[position // self.block_size]
                block_offset = position % self.block_size
                slot = block_number * self.block_size + block_offset
                slot_mapping.append(slot)
                lora_index_mapping.append(lora_id)
                lora_prompt_mapping.append(lora_id)

                if self.sliding_window is not None:
                    sliding_window_blocks = (self.sliding_window //
                                             self.block_size)
                    block_table = block_table[-sliding_window_blocks:]
                block_tables.append(block_table)

        # vLLM uses cuda graph only for decoding requests.
        # See `capture_model` API for more details.
        # For decoding requests, batch_size == input_tokens.
        batch_size = len(input_tokens)
        max_context_len = max(context_lens)
        use_captured_graph = (
            not self.model_config.enforce_eager
            and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1]
            and max_context_len <= self.max_context_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)
                context_lens.append(1)
                block_tables.append([])
                lora_index_mapping.append(0)
            batch_size = graph_batch_size

        input_tokens = torch.tensor(input_tokens,
                                    dtype=torch.long,
                                    device=self.device)
        input_positions = torch.tensor(input_positions,
                                       dtype=torch.long,
                                       device=self.device)
        slot_mapping = torch.tensor(slot_mapping,
                                    dtype=torch.long,
                                    device=self.device)
        context_lens = torch.tensor(context_lens,
                                    dtype=torch.int,
                                    device=self.device)

        if use_captured_graph:
            # When using cuda-graph all these tensors should be
            # padded.
            assert context_lens.shape[0] == input_tokens.shape[0]
            assert context_lens.shape[0] == input_positions.shape[0]
            assert context_lens.shape[0] == slot_mapping.shape[0]

            # 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,
            )

        attn_metadata = self.attn_backend.make_metadata(
            is_prompt=False,
            slot_mapping=slot_mapping,
            prompt_lens=None,
            prompt_lens_tensor=None,
            num_prompt_tokens=0,
            num_generation_tokens=len(input_tokens),
            max_subquery_len=None,
            max_context_len=max_context_len,
            max_prompt_len=None,
            subquery_start_loc=None,
            seq_start_loc=None,
            context_lens=context_lens,
            block_tables=block_tables,
            use_cuda_graph=use_captured_graph,
            kv_cache_dtype=self.kv_cache_dtype,
        )
        return (input_tokens, input_positions, attn_metadata,
                lora_index_mapping, lora_prompt_mapping, lora_requests)

    def _prepare_sample(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        prompt_lens: List[int],
        subquery_lens: Optional[List[int]],
    ) -> SamplingMetadata:
        seq_groups: List[Tuple[List[int], SamplingParams]] = []
        selected_token_indices: List[int] = []
        generators: List[torch.Generator] = []
        selected_token_start_idx = 0
        categorized_sample_indices = {t: [] for t in SamplingType}
        categorized_sample_indices_start_idx = 0
        categorized_sampled_token_indices_start_idx = 0

        for i, seq_group_metadata in enumerate(seq_group_metadata_list):
            seq_ids = list(seq_group_metadata.seq_data.keys())
            sampling_params = seq_group_metadata.sampling_params
            seq_groups.append((seq_ids, sampling_params))

            if seq_group_metadata.is_prompt:
                assert len(seq_ids) == 1
                assert subquery_lens is not None
                subquery_len = subquery_lens[i]
                if sampling_params.prompt_logprobs is not None:
                    # NOTE: prompt token positions do not need sample, skip
                    categorized_sample_indices_start_idx += subquery_len - 1

                categorized_sample_indices[
                    sampling_params.sampling_type].append([
                        categorized_sample_indices_start_idx,
                        categorized_sampled_token_indices_start_idx
                    ])
                categorized_sample_indices_start_idx += 1
                categorized_sampled_token_indices_start_idx += 1

                if sampling_params.prompt_logprobs is not None:
                    selected_token_indices.extend(
                        range(selected_token_start_idx,
                              selected_token_start_idx + subquery_len - 1))
                selected_token_indices.append(selected_token_start_idx +
                                              subquery_len - 1)
                selected_token_start_idx += subquery_len

                if sampling_params.seed is not None:
                    seq_group_metadata.state.generator = torch.Generator(
                        device=self.device).manual_seed(sampling_params.seed)
            else:
                num_seqs = len(seq_ids)
                selected_token_indices.extend(
                    range(selected_token_start_idx,
                          selected_token_start_idx + num_seqs))
                selected_token_start_idx += num_seqs

                categorized_sample_indices[
                    sampling_params.sampling_type].extend(
                        zip(
                            range(
                                categorized_sample_indices_start_idx,
                                categorized_sample_indices_start_idx +
                                num_seqs),
                            range(
                                categorized_sampled_token_indices_start_idx,
                                categorized_sampled_token_indices_start_idx +
                                num_seqs)))
                categorized_sample_indices_start_idx += num_seqs
                categorized_sampled_token_indices_start_idx += num_seqs

            if sampling_params.seed is not None:
                generators.append(seq_group_metadata.state.generator)

        selected_token_indices = async_tensor_h2d(selected_token_indices,
                                                  dtype=torch.long,
                                                  target_device=self.device,
                                                  pin_memory=self.pin_memory)

        categorized_sample_indices = {
            t: maybe_expand_dim(
                async_tensor_h2d(seq_ids,
                                 dtype=torch.int,
                                 target_device=self.device,
                                 pin_memory=self.pin_memory), 2, 2)
            for t, seq_ids in categorized_sample_indices.items()
        }

        seq_data: Dict[int, SequenceData] = {}
        for seq_group_metadata in seq_group_metadata_list:
            seq_data.update(seq_group_metadata.seq_data)

        sampling_metadata = SamplingMetadata(
            seq_groups=seq_groups,
            seq_data=seq_data,
            prompt_lens=prompt_lens,
            selected_token_indices=selected_token_indices,
            categorized_sample_indices=categorized_sample_indices,
            generators=generators,
        )
        return sampling_metadata

    def prepare_input_tensors(
        self,
        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata,
               Set[int], LoRAMapping, torch.Tensor]:
        if self.is_driver_worker:
            # NOTE: We assume that all sequences in the group are all prompts or
            # all decodes.
            is_prompt = seq_group_metadata_list[0].is_prompt
            # Prepare input tensors.
            if is_prompt:
                (input_tokens, input_positions, attn_metadata, prompt_lens,
                 subquery_lens, lora_index_mapping, lora_prompt_mapping,
                 lora_requests, multi_modal_input
                 ) = self._prepare_prompt(seq_group_metadata_list)
            else:
                (input_tokens, input_positions, attn_metadata,
                 lora_index_mapping, lora_prompt_mapping,
                 lora_requests) = self._prepare_decode(seq_group_metadata_list)
                prompt_lens = []
                subquery_lens = None
                multi_modal_input = None
            sampling_metadata = self._prepare_sample(seq_group_metadata_list,
                                                     prompt_lens,
                                                     subquery_lens)

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

            # Broadcast the metadata.
            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,
            }
            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")
            attn_metadata = self.attn_backend.make_metadata(**metadata_dict)
            sampling_metadata = SamplingMetadata(
                seq_groups=None,
                seq_data=None,
                prompt_lens=None,
                selected_token_indices=selected_token_indices,
                categorized_sample_indices=None,
                generators=None,
                perform_sampling=False,
            )

        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: Optional[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)

        # Execute the model.
        if attn_metadata.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 sampling_metadata.perform_sampling:
            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:
            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
        # vLLM 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) -> bool:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
        return self.lora_manager.remove_all_loras()

    def set_active_loras(self, lora_requests: List[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.
        """
        # NOTE(woosuk): This is a hack to ensure that the NCCL backend is never
        # deleted before the CUDA graphs.
        self.pynccl_backend = pynccl_utils.get_nccl_backend()

        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)
        context_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
        ]

        # NOTE(woosuk): There are 3 backends for all-reduce: custom all-reduce
        # kernel, pynccl, and PyTorch NCCL. When using CUDA graph, we use
        # either custom all-reduce kernel or pynccl. When not using CUDA
        # graph, we use either custom all-reduce kernel or PyTorch NCCL.
        # We always prioritize using custom all-reduce kernel but fall back
        # to PyTorch or pynccl if it is disabled or not supported.
        with custom_all_reduce.capture():
            # 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(
                    is_prompt=False,
                    slot_mapping=slot_mapping[:batch_size],
                    prompt_lens=None,
                    prompt_lens_tensor=None,
                    num_prompt_tokens=0,
                    num_generation_tokens=batch_size,
                    max_subquery_len=None,
                    max_context_len=self.max_context_len_to_capture,
                    max_prompt_len=None,
                    subquery_start_loc=None,
                    seq_start_loc=None,
                    context_lens=context_lens[:batch_size],
                    block_tables=block_tables[:batch_size],
                    use_cuda_graph=True,
                    kv_cache_dtype=self.kv_cache_dtype,
                )

                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,
                )
                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:.0f} secs.")

    def __del__(self) -> None:
        # Delete the CUDA graphs before deleting the pynccl communicator.
        # NOTE(woosuk): This is necessary because otherwise deadlocks can
        # happen.
        # FIXME(woosuk): This is a bit hacky. Find a more robust solution.
        # TODO(youkaichao): 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.graph = None
        self.input_buffers: Dict[str, torch.Tensor] = {}
        self.output_buffers: Dict[str, torch.Tensor] = {}

    def capture(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        memory_pool,
        **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).
        with _maybe_pynccl():
            self.model(
                input_ids,
                positions,
                kv_caches,
                attn_metadata,
                **kwargs,
            )
        torch.cuda.synchronize()

        # Capture the graph.
        # NOTE(woosuk): Python 3.8 does not support multi-line with statements.
        # https://stackoverflow.com/questions/31039022/python-multi-line-with-statement
        self.graph = torch.cuda.CUDAGraph()
        with torch.cuda.graph(self.graph, pool=memory_pool):  # noqa: SIM117
            with _maybe_pynccl():
                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,
            "context_lens": attn_metadata.context_lens,
            "block_tables": attn_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["context_lens"].copy_(attn_metadata.context_lens,
                                                 non_blocking=True)
        self.input_buffers["block_tables"].copy_(attn_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)


@contextlib.contextmanager
def _maybe_pynccl():
    if pynccl_utils.is_initialized(
    ) and not custom_all_reduce.is_initialized():
        with with_pynccl_for_all_reduce():
            yield
    else:
        yield


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
