lib.rs

// SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
// SPDX-License-Identifier: Apache-2.0
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

use std::collections::HashMap;
use std::{num::NonZero, path::Path, sync::Arc};

use async_openai::types::FinishReason;
use async_stream::stream;
use async_trait::async_trait;
use either::Either;
use indexmap::IndexMap;
use mistralrs::{
    AutoDeviceMapParams, Constraint, DefaultSchedulerMethod, Device, DeviceMapSetting,
    GGUFLoaderBuilder, GGUFSpecificConfig, MemoryGpuConfig, MistralRs, MistralRsBuilder,
    ModelDType, NormalLoaderBuilder, NormalRequest, NormalSpecificConfig, PagedAttentionConfig,
    Request, RequestMessage, ResponseOk, SamplingParams, SchedulerConfig, StopTokens, TokenSource,
};
use tokio::sync::mpsc::channel;

use dynamo_runtime::engine::{AsyncEngine, AsyncEngineContextProvider, ResponseStream};
use dynamo_runtime::pipeline::error as pipeline_error;
use dynamo_runtime::pipeline::{Error, ManyOut, SingleIn};
use dynamo_runtime::protocols::annotated::Annotated;

use dynamo_llm::protocols::openai::chat_completions::{
    NvCreateChatCompletionRequest, NvCreateChatCompletionStreamResponse,
};
use dynamo_llm::types::openai::chat_completions::OpenAIChatCompletionsStreamingEngine;

/// How many requests mistral will run at once in the paged attention scheduler.
/// It actually runs 1 fewer than this.
/// I would call this the batch size but apparently that's something else.
const PAGED_ATTENTION_MAX_NUM_SEQS: usize = 10;

/// Experimental: Switch this to true to enable paged attention on CUDA devices.
/// Under load (dynamo-run batch mode) paged attention sometimes returns an immediate
/// finish_reason=stop and no tokens for one of the requests.
const EXP_ENABLE_PAGED_ATTENTION: bool = false;

pub async fn make_engine(
    gguf_path: &Path,
) -> pipeline_error::Result<OpenAIChatCompletionsStreamingEngine> {
    let engine = MistralRsEngine::new(gguf_path).await?;
    let engine: OpenAIChatCompletionsStreamingEngine = Arc::new(engine);
    Ok(engine)
}

/// Gets the best device, cpu, cuda if compiled with CUDA
fn best_device() -> pipeline_error::Result<Device> {
    #[cfg(not(feature = "metal"))]
    {
        Ok(Device::cuda_if_available(0)?)
    }
    #[cfg(feature = "metal")]
    {
        Ok(Device::new_metal(0)?)
    }
}

struct MistralRsEngine {
    mistralrs: Arc<MistralRs>,
}

impl MistralRsEngine {
    async fn new(model_path: &Path) -> pipeline_error::Result<Self> {
        let loader = if model_path.is_file() {
            // Load from a GGUF
            let Some(model_filename) = model_path.file_name() else {
                pipeline_error::bail!("Missing filename in model path");
            };
            let Some(model_dir) = model_path.parent() else {
                pipeline_error::bail!("Invalid model path");
            };

            GGUFLoaderBuilder::new(
                None,
                None,
                model_dir.display().to_string(),
                vec![model_filename.to_string_lossy().into_owned()],
                GGUFSpecificConfig {
                    prompt_chunksize: None,
                    topology: None,
                },
            )
            .build()
        } else {
            // Load from a HF repo dir
            NormalLoaderBuilder::new(
                NormalSpecificConfig {
                    use_flash_attn: false,
                    prompt_chunksize: None,
                    topology: None,
                    organization: Default::default(),
                    write_uqff: None,
                    from_uqff: None,
                    imatrix: None,
                    calibration_file: None,
                },
                None,
                None,
                Some(model_path.display().to_string()),
            )
            .build(None)?
        };

        let max_seq_len = AutoDeviceMapParams::DEFAULT_MAX_SEQ_LEN;

        // Paged attention requires cuda
        let paged_attention_config = if cfg!(feature = "cuda") && EXP_ENABLE_PAGED_ATTENTION {
            Some(PagedAttentionConfig::new(
                None, // Block size, default 32
                4096, // CPU memory in MiB
                MemoryGpuConfig::ContextSize(max_seq_len),
            )?)
        } else {
            None
        };
        // Load, into a Pipeline
        let pipeline = loader.load_model_from_hf(
            None,
            TokenSource::None, // The model was already downloaded
            &ModelDType::Auto,
            &best_device()?,
            false,
            DeviceMapSetting::Auto(AutoDeviceMapParams::Text {
                max_seq_len,
                max_batch_size: AutoDeviceMapParams::DEFAULT_MAX_BATCH_SIZE,
            }),
            None,
            paged_attention_config,
        )?;
        let scheduler = if cfg!(feature = "cuda") && EXP_ENABLE_PAGED_ATTENTION {
            tracing::debug!("Using mistralrs PagedAttentionMeta scheduler");
            let config = match pipeline.lock().await.get_metadata().cache_config.as_ref() {
                Some(conf) => conf.clone(),
                None => {
                    anyhow::bail!("Failed loading model config");
                }
            };
            SchedulerConfig::PagedAttentionMeta {
                max_num_seqs: PAGED_ATTENTION_MAX_NUM_SEQS,
                config,
            }
        } else {
            tracing::debug!("Using mistralrs DefaultScheduler");
            SchedulerConfig::DefaultScheduler {
                // Safety: unwrap trivially safe here
                method: DefaultSchedulerMethod::Fixed(NonZero::new(max_seq_len).unwrap()),
            }
        };
        // Create the MistralRs, which is a runner
        let builder = MistralRsBuilder::new(pipeline.clone(), scheduler).with_prefix_cache_n(16);
        let engine = MistralRsEngine {
            mistralrs: builder.build(),
        };

        // skip the id used for dummy run https://github.com/EricLBuehler/mistral.rs/issues/1218
        let _ = engine.mistralrs.next_request_id();

        // Perform warmup request
        let (tx, mut rx) = channel(1);
        let request_id = engine.mistralrs.next_request_id();
        let warmup_request = Request::Normal(NormalRequest {
            id: request_id,
            messages: RequestMessage::Chat(vec![IndexMap::from([
                ("role".to_string(), Either::Left("user".to_string())),
                ("content".to_string(), Either::Left("test".to_string())),
            ])]),
            sampling_params: SamplingParams::deterministic(),
            response: tx,
            return_logprobs: false,
            is_streaming: false,
            constraint: Constraint::None,
            suffix: None,
            adapters: None,
            tools: None,
            tool_choice: None,
            logits_processors: None,
            return_raw_logits: false,
        });

        // Send warmup request and consume response
        if let Ok(sender) = engine.mistralrs.get_sender() {
            if let Ok(()) = sender.send(warmup_request).await {
                if let Some(response) = rx.recv().await {
                    match response.as_result() {
                        Ok(r) => {
                            tracing::debug!(request_id, "Warmup response: {r:?}");
                        }
                        Err(err) => {
                            tracing::error!(request_id, %err, "Failed converting response to result.");
                        }
                    }
                }
            }
        }

        Ok(engine)
    }
}

#[async_trait]
impl
    AsyncEngine<
        SingleIn<NvCreateChatCompletionRequest>,
        ManyOut<Annotated<NvCreateChatCompletionStreamResponse>>,
        Error,
    > for MistralRsEngine
{
    async fn generate(
        &self,
        request: SingleIn<NvCreateChatCompletionRequest>,
    ) -> Result<ManyOut<Annotated<NvCreateChatCompletionStreamResponse>>, Error> {
        let (request, context) = request.transfer(());
        let ctx = context.context();
        let (tx, mut rx) = channel(10_000);

        let mut messages = vec![];
        for m in request.inner.messages {
            let async_openai::types::ChatCompletionRequestMessage::User(inner_m) = m else {
                continue;
            };
            let async_openai::types::ChatCompletionRequestUserMessageContent::Text(content) =
                inner_m.content
            else {
                anyhow::bail!("Only Text type chat completion supported");
            };
            let r = IndexMap::from([
                ("role".to_string(), Either::Left("user".to_string())),
                ("content".to_string(), Either::Left(content)),
            ]);
            messages.push(r);
        }
        if messages.is_empty() {
            anyhow::bail!("Empty request");
        }

        let det = SamplingParams::deterministic();
        // allow deprecated because max_tokens
        #[allow(deprecated)]
        let sampling_params = SamplingParams {
            temperature: request
                .inner
                .temperature
                .map(|t| t as f64)
                .or(det.temperature),
            top_p: request.inner.top_p.map(|t| t as f64).or(det.top_p),
            top_n_logprobs: request
                .inner
                .top_logprobs
                .map(|t| t as usize)
                .unwrap_or(det.top_n_logprobs),
            frequency_penalty: request.inner.frequency_penalty.or(det.frequency_penalty),
            presence_penalty: request.inner.presence_penalty.or(det.presence_penalty),
            stop_toks: request.inner.stop.map(to_stop_tokens).or(det.stop_toks),
            max_len: request
                .inner
                .max_completion_tokens
                .or(request.inner.max_tokens)
                .map(|m| m as usize)
                .or(det.max_len),
            logits_bias: request
                .inner
                .logit_bias
                .map(to_logit_bias)
                .or(det.logits_bias),
            // These are not in async-openai yet
            top_k: det.top_k,
            min_p: det.min_p,
            n_choices: 1,
            dry_params: det.dry_params,
        };
        let request_id = self.mistralrs.next_request_id();
        let mistralrs_request = Request::Normal(NormalRequest {
            id: request_id,
            messages: RequestMessage::Chat(messages),
            sampling_params,
            response: tx,
            return_logprobs: request.inner.logprobs.unwrap_or_default(),
            is_streaming: true,
            constraint: Constraint::None,
            suffix: None,
            adapters: None,
            tools: None,
            tool_choice: None,
            logits_processors: None,
            return_raw_logits: false,
        });

        self.mistralrs.get_sender()?.send(mistralrs_request).await?;

        let output = stream! {
            while let Some(response) = rx.recv().await {
                let response = match response.as_result() {
                    Ok(r) => r,
                    Err(err) => {
                        tracing::error!(request_id, %err, "Failed converting mistralrs channel response to result.");
                        break;
                    }
                };
                match response {
                    ResponseOk::Chunk(c) => {
                        let Some(from_assistant) = c.choices[0].delta.content.clone() else {
                            tracing::warn!(request_id, "No content from mistralrs. Abandoning request.");
                            break;
                        };
                        let finish_reason = match &c.choices[0].finish_reason.as_deref() {
                            Some("stop") | Some("canceled") => {
                                Some(FinishReason::Stop)
                            }
                            Some("length") => {
                                Some(FinishReason::Length)
                            }
                            Some(s) => {
                                tracing::warn!(request_id, stop_reason = s, "Unknow stop reason");
                                Some(FinishReason::Stop)
                            }
                            None => None,
                        };
                        //tracing::trace!("from_assistant: {from_assistant}");

                        #[allow(deprecated)]
                        let inner = async_openai::types::CreateChatCompletionStreamResponse{
                            id: c.id,
                            choices: vec![async_openai::types::ChatChoiceStream{
                                index: 0,
                                delta: async_openai::types::ChatCompletionStreamResponseDelta{
                                    //role: c.choices[0].delta.role,
                                    role: Some(async_openai::types::Role::Assistant),
                                    content: Some(from_assistant),
                                    tool_calls: None,
                                    refusal: None,
                                    function_call: None,
                                },
                                logprobs: None,
                                finish_reason,
                            }],
                            model: c.model,
                            created: c.created as u32,
                            object: c.object.clone(),
                            usage: None,
                            system_fingerprint: Some(c.system_fingerprint),
                            service_tier: None,
                        };
                        let delta = NvCreateChatCompletionStreamResponse{inner};
                        let ann = Annotated{
                            id: None,
                            data: Some(delta),
                            event: None,
                            comment: None,
                        };
                        yield ann;

                        if finish_reason.is_some() {
                            //tracing::trace!(request_id, "Finish reason: {finish_reason:?}");
                            break;
                        }
                    },
                    x => tracing::error!(request_id, "Unhandled. {x:?}"),
                }
            }
        };
        Ok(ResponseStream::new(Box::pin(output), ctx))
    }
}

/// openai stop tokens to mistralrs stop tokens
fn to_stop_tokens(t: async_openai::types::Stop) -> StopTokens {
    match t {
        async_openai::types::Stop::String(s) => StopTokens::Seqs(vec![s]),
        async_openai::types::Stop::StringArray(v) => StopTokens::Seqs(v),
    }
}

/// openai logit bias (strings/json) to mistralrs (u32/f32)
/// I think the input looks like this: {"3721": -100, "17765": 100}
fn to_logit_bias(lb: HashMap<String, serde_json::Value>) -> HashMap<u32, f32> {
    let mut out = HashMap::new();
    for (key, value) in &lb {
        let token_id: u32 = match key.parse() {
            Ok(t) => t,
            Err(err) => {
                tracing::warn!(
                    "Unexpected logit_bias map. Key '{key}' is not an int: {lb:?}. {err}."
                );
                return HashMap::new();
            }
        };
        let Some(bias) = value.as_f64() else {
            tracing::warn!("Unexpected logit_bias map. Value '{value}' is not a float: {lb:?}");
            return HashMap::new();
        };
        out.insert(token_id, bias as f32);
    }
    out
}