cudagraph_utils.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Callable, Iterable
from typing import Any

import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm

from vllm.config import VllmConfig
from vllm.config.compilation import CUDAGraphMode
from vllm.distributed.parallel_state import graph_capture, is_global_first_rank
from vllm.forward_context import set_forward_context
from vllm.v1.attention.backend import AttentionMetadataBuilder
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.worker.gpu.attn_utils import (
    build_attn_metadata,
    build_slot_mappings_by_layer,
)
from vllm.v1.worker.gpu.block_table import BlockTables
from vllm.v1.worker.gpu.dp_utils import make_num_tokens_across_dp
from vllm.v1.worker.gpu.input_batch import InputBuffers


class CudaGraphManager:
    def __init__(
        self,
        vllm_config: VllmConfig,
        uses_mrope: bool,
        device: torch.device,
    ):
        self.vllm_config = vllm_config
        self.scheduler_config = vllm_config.scheduler_config
        self.uses_mrope = uses_mrope
        self.device = device

        self.max_model_len = vllm_config.model_config.max_model_len
        self.max_num_reqs = self.scheduler_config.max_num_seqs
        self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
        self.dp_size = vllm_config.parallel_config.data_parallel_size
        self.compilation_config = vllm_config.compilation_config
        assert self.compilation_config is not None
        self.cudagraph_mode: CUDAGraphMode
        if self.compilation_config.cudagraph_mode is None:
            self.cudagraph_mode = CUDAGraphMode.NONE
        else:
            self.cudagraph_mode = self.compilation_config.cudagraph_mode
        self.cudagraph_sizes = get_cudagraph_sizes(
            self.compilation_config.cudagraph_capture_sizes,
            self.max_num_reqs,
            self.max_num_tokens,
            self.cudagraph_mode,
        )

        self.graphs: dict[int, torch.cuda.CUDAGraph] = {}
        self.pool = torch.cuda.graph_pool_handle()
        self.hidden_states: torch.Tensor | None = None

    def needs_capture(self) -> bool:
        return len(self.cudagraph_sizes) > 0

    def get_cudagraph_size(
        self,
        num_tokens_after_padding: int,
        num_tokens_per_request: Iterable[int],
    ) -> int | None:
        return get_cudagraph_size(
            num_tokens_after_padding,
            num_tokens_per_request,
            self.cudagraph_sizes,
            self.cudagraph_mode,
        )

    def capture_graph(
        self,
        num_tokens: int,
        model: nn.Module,
        input_buffers: InputBuffers,
        mrope_positions: torch.Tensor | None,
        inputs_embeds: torch.Tensor | None,
        block_tables: BlockTables,
        attn_metadata_builders: list[AttentionMetadataBuilder],
        kv_cache_config: KVCacheConfig,
    ) -> None:
        num_reqs = min(num_tokens, self.max_num_reqs)
        input_ids = input_buffers.input_ids[:num_tokens]
        positions = input_buffers.positions[:num_tokens]
        if self.uses_mrope:
            assert mrope_positions is not None
            positions = mrope_positions[:, :num_tokens]
        if inputs_embeds is not None:
            inputs_embeds = inputs_embeds[:num_tokens]
        attn_metadata, slot_mappings = prepare_inputs_to_capture(
            num_reqs,
            num_tokens,
            input_buffers,
            block_tables,
            attn_metadata_builders,
            self.max_model_len,
            kv_cache_config,
        )
        num_tokens_across_dp = make_num_tokens_across_dp(self.dp_size, num_tokens)

        # Warm up.
        with set_forward_context(
            attn_metadata,
            self.vllm_config,
            num_tokens=num_tokens,
            cudagraph_runtime_mode=CUDAGraphMode.NONE,
            num_tokens_across_dp=num_tokens_across_dp,
            slot_mapping=slot_mappings,
        ):
            hidden_states = model(
                input_ids=input_ids,
                positions=positions,
                inputs_embeds=inputs_embeds,
            )
            if self.hidden_states is None:
                self.hidden_states = torch.empty_like(hidden_states)

        # Capture the graph.
        assert num_tokens not in self.graphs
        graph = torch.cuda.CUDAGraph()
        with (
            set_forward_context(
                attn_metadata,
                self.vllm_config,
                num_tokens=num_tokens,
                cudagraph_runtime_mode=CUDAGraphMode.NONE,
                num_tokens_across_dp=num_tokens_across_dp,
                slot_mapping=slot_mappings,
            ),
            torch.cuda.graph(graph, self.pool),
        ):
            hidden_states = model(
                input_ids=input_ids,
                positions=positions,
                inputs_embeds=inputs_embeds,
            )
            self.hidden_states[:num_tokens] = hidden_states
        self.graphs[num_tokens] = graph

    @torch.inference_mode()
    def capture(
        self,
        model: nn.Module,
        input_buffers: InputBuffers,
        mrope_positions: torch.Tensor | None,
        inputs_embeds: torch.Tensor | None,
        block_tables: BlockTables,
        attn_metadata_builders: list[AttentionMetadataBuilder],
        kv_cache_config: KVCacheConfig,
    ) -> None:
        capture_graphs(
            self.cudagraph_sizes,
            self.device,
            self.capture_graph,
            model=model,
            input_buffers=input_buffers,
            mrope_positions=mrope_positions,
            inputs_embeds=inputs_embeds,
            block_tables=block_tables,
            attn_metadata_builders=attn_metadata_builders,
            kv_cache_config=kv_cache_config,
        )

    def run(self, num_tokens: int) -> torch.Tensor:
        assert num_tokens in self.graphs
        self.graphs[num_tokens].replay()
        assert self.hidden_states is not None
        return self.hidden_states[:num_tokens]


def get_cudagraph_sizes(
    capture_sizes: list[int] | None,
    max_num_reqs: int,
    max_num_tokens: int,
    cudagraph_mode: CUDAGraphMode,
) -> dict[int, int]:
    if not cudagraph_mode.has_full_cudagraphs():
        return {}
    if not capture_sizes:
        return {}

    capture_sizes = sorted(capture_sizes)
    # Limit the capture sizes to the max number of requests or tokens.
    upper_bound = (
        max_num_reqs
        if cudagraph_mode == CUDAGraphMode.FULL_DECODE_ONLY
        else max_num_tokens
    )
    capture_sizes = [x for x in capture_sizes if x <= upper_bound]
    if not capture_sizes:
        return {}

    cudagraph_sizes: dict[int, int] = {}
    for i in range(1, capture_sizes[-1] + 1):
        for x in capture_sizes:
            if i <= x:
                cudagraph_sizes[i] = x
                break
    return cudagraph_sizes


def get_cudagraph_size(
    num_tokens_after_dp_padding: int,
    num_tokens_per_request: Iterable[int],
    cudagraph_sizes: dict[int, int],
    cudagraph_mode: CUDAGraphMode,
) -> int | None:
    if not cudagraph_mode.has_full_cudagraphs():
        # No full CUDA graph is used.
        return None

    size = cudagraph_sizes.get(num_tokens_after_dp_padding)
    if size is None:
        # No CUDA graph for this size.
        return None

    is_mixed = any(x > 1 for x in num_tokens_per_request)
    if is_mixed and cudagraph_mode.mixed_mode() != CUDAGraphMode.FULL:
        # Prefill is included, and this mode doesn't use CUDA graph for it.
        return None
    return size


def capture_graphs(
    cudagraph_sizes: dict[int, int],
    device: torch.device,
    capture_fn: Callable,
    **capture_kwargs,
) -> None:
    # Capture larger graphs first.
    sizes_to_capture = sorted(set(cudagraph_sizes.values()), reverse=True)
    if is_global_first_rank():
        sizes_to_capture = tqdm(sizes_to_capture, desc="Capturing CUDA graphs")

    with graph_capture(device=device):
        for size in sizes_to_capture:
            capture_fn(size, **capture_kwargs)


def prepare_inputs_to_capture(
    num_reqs: int,
    num_tokens: int,
    input_buffers: InputBuffers,
    block_tables: BlockTables,
    attn_metadata_builders: list[AttentionMetadataBuilder],
    max_model_len: int,
    kv_cache_config: KVCacheConfig,
) -> tuple[dict[str, Any], dict[str, torch.Tensor]]:
    num_tokens_per_req = num_tokens // num_reqs

    query_start_loc_np = np.arange(num_reqs + 1, dtype=np.int32) * num_tokens_per_req
    query_start_loc_np[-1] = num_tokens
    query_start_loc_cpu = torch.from_numpy(query_start_loc_np)
    input_buffers.query_start_loc[: num_reqs + 1] = query_start_loc_cpu
    input_buffers.query_start_loc[num_reqs + 1 :] = num_tokens
    query_start_loc = input_buffers.query_start_loc[: num_reqs + 1]

    # HACK(woosuk): For faster warmup, we set seq_lens (GPU) to num_tokens
    # rather than max_model_len.
    input_buffers.seq_lens[:num_reqs] = num_tokens
    input_buffers.seq_lens[num_reqs:] = 0

    input_block_tables = [x[:num_reqs] for x in block_tables.input_block_tables]
    slot_mappings = block_tables.slot_mappings[:, :num_tokens]
    slot_mappings_by_layer = build_slot_mappings_by_layer(
        slot_mappings, kv_cache_config
    )

    attn_metadata = build_attn_metadata(
        attn_metadata_builders=attn_metadata_builders,
        num_reqs=num_reqs,
        num_tokens=num_tokens,
        query_start_loc_gpu=query_start_loc,
        query_start_loc_cpu=query_start_loc_cpu,
        seq_lens=input_buffers.seq_lens,
        max_seq_len=max_model_len,
        block_tables=input_block_tables,
        slot_mappings=slot_mappings,
        kv_cache_config=kv_cache_config,
    )
    return attn_metadata, slot_mappings_by_layer