mistralrs_core/pipeline/
ggml.rs

1use super::llg::build_llg_factory;
2use super::{
3    get_model_paths, get_xlora_paths, text_models_inputs_processor::ModelInputs, AdapterKind,
4    CacheManager, GeneralMetadata, Loader, ModelKind, ModelPaths, QuantizationKind, TokenSource,
5};
6use super::{
7    AnyMoePipelineMixin, CacheManagerMixin, EitherCache, ForwardInputsResult, IsqPipelineMixin,
8    MetadataMixin, ModelCategory, PreProcessingMixin,
9};
10use crate::device_map::DeviceMapper;
11use crate::kv_cache::FullCacheManager;
12use crate::lora::Ordering;
13use crate::pipeline::chat_template::{calculate_eos_tokens, GenerationConfig};
14use crate::pipeline::inputs_processor::DEFAULT_PROMPT_CHUNK_SIZE;
15use crate::pipeline::sampling::sample_and_add_toks;
16use crate::pipeline::{get_chat_template, Modalities, SupportedModality};
17use crate::pipeline::{ChatTemplate, LocalModelPaths};
18use crate::prefix_cacher::PrefixCacheManagerV2;
19use crate::sequence::Sequence;
20use crate::utils::debug::DeviceRepr;
21use crate::utils::model_config as ModelConfig;
22use crate::utils::tokenizer::get_tokenizer;
23use crate::xlora_models::NonGranularState;
24use crate::{
25    get_mut_arcmutex, get_paths, DeviceMapSetting, PagedAttentionConfig, Pipeline, Topology,
26    TryIntoDType, DEBUG,
27};
28use crate::{
29    models::quantized_llama::ModelWeights as QLlama, utils::tokens::get_token,
30    xlora_models::XLoraQLlama,
31};
32use anyhow::Result;
33use candle_core::quantized::ggml_file;
34use candle_core::{Device, Tensor};
35use hf_hub::{api::sync::ApiBuilder, Repo, RepoType};
36use mistralrs_quant::IsqType;
37use rand_isaac::Isaac64Rng;
38use std::any::Any;
39use std::fs;
40use std::num::{NonZero, NonZeroUsize};
41use std::path::PathBuf;
42use std::str::FromStr;
43use std::sync::Arc;
44use tokenizers::Tokenizer;
45use tokio::sync::Mutex;
46use tracing::{info, warn};
47
48enum Model {
49    Llama(QLlama),
50    XLoraLlama(Box<XLoraQLlama>),
51}
52
53pub struct GGMLPipeline {
54    model: Model,
55    tokenizer: Arc<Tokenizer>,
56    no_kv_cache: bool,
57    chat_template: Arc<ChatTemplate>,
58    model_id: String,
59    non_granular_state: Option<NonGranularState>,
60    metadata: Arc<GeneralMetadata>,
61}
62
63/// A loader for a GGML model.
64pub struct GGMLLoader {
65    model_id: String,
66    config: GGMLSpecificConfig,
67    quantized_model_id: Option<String>,
68    quantized_filename: Option<String>,
69    xlora_model_id: Option<String>,
70    xlora_order: Option<Ordering>,
71    no_kv_cache: bool,
72    chat_template: Option<String>,
73    tokenizer_json: Option<String>,
74    kind: ModelKind,
75    tgt_non_granular_index: Option<usize>,
76    jinja_explicit: Option<String>,
77    lora_adapter_ids: Option<Vec<String>>,
78}
79
80#[derive(Clone, Default)]
81/// Config for a GGML loader.
82pub struct GGMLSpecificConfig {
83    pub gqa: usize,
84    pub prompt_chunksize: Option<NonZeroUsize>,
85    pub topology: Option<Topology>,
86}
87
88#[derive(Default)]
89/// A builder for a GGML loader.
90pub struct GGMLLoaderBuilder {
91    model_id: Option<String>,
92    config: GGMLSpecificConfig,
93    quantized_model_id: String,
94    quantized_filename: String,
95    xlora_model_id: Option<String>,
96    kind: ModelKind,
97    xlora_order: Option<Ordering>,
98    no_kv_cache: bool,
99    chat_template: Option<String>,
100    tokenizer_json: Option<String>,
101    tgt_non_granular_index: Option<usize>,
102    jinja_explicit: Option<String>,
103}
104
105impl GGMLLoaderBuilder {
106    #[allow(clippy::too_many_arguments)]
107    pub fn new(
108        config: GGMLSpecificConfig,
109        chat_template: Option<String>,
110        tokenizer_json: Option<String>,
111        model_id: Option<String>,
112        quantized_model_id: String,
113        quantized_filename: String,
114        no_kv_cache: bool,
115        jinja_explicit: Option<String>,
116    ) -> Self {
117        let kind = ModelKind::GgufQuantized {
118            quant: QuantizationKind::Ggml,
119        };
120
121        Self {
122            config,
123            chat_template,
124            tokenizer_json,
125            model_id,
126            kind,
127            quantized_filename,
128            quantized_model_id,
129            no_kv_cache,
130            jinja_explicit,
131            ..Default::default()
132        }
133    }
134
135    fn with_adapter(
136        mut self,
137        xlora_model_id: String,
138        xlora_order: Ordering,
139        no_kv_cache: bool,
140        tgt_non_granular_index: Option<usize>,
141    ) -> Self {
142        self.xlora_model_id = Some(xlora_model_id);
143        self.xlora_order = Some(xlora_order);
144        self.no_kv_cache = no_kv_cache;
145        self.tgt_non_granular_index = tgt_non_granular_index;
146        self.model_id = if let Some(id) = self.model_id {
147            Some(id)
148        } else {
149            info!(
150                "Using adapter base model ID: `{}`",
151                self.xlora_order.as_ref().unwrap().base_model_id
152            );
153            Some(self.xlora_order.as_ref().unwrap().base_model_id.clone())
154        };
155        self
156    }
157
158    pub fn with_xlora(
159        mut self,
160        xlora_model_id: String,
161        xlora_order: Ordering,
162        no_kv_cache: bool,
163        tgt_non_granular_index: Option<usize>,
164    ) -> Self {
165        self.kind = (AdapterKind::XLora, QuantizationKind::Ggml).into();
166
167        self.with_adapter(
168            xlora_model_id,
169            xlora_order,
170            no_kv_cache,
171            tgt_non_granular_index,
172        )
173    }
174
175    pub fn with_lora(mut self, lora_model_id: String, lora_order: Ordering) -> Self {
176        self.kind = (AdapterKind::Lora, QuantizationKind::Ggml).into();
177
178        self.with_adapter(lora_model_id, lora_order, false, None)
179    }
180
181    pub fn build(self) -> Box<dyn Loader> {
182        Box::new(GGMLLoader {
183            model_id: self.model_id.unwrap(),
184            config: self.config,
185            xlora_model_id: self.xlora_model_id,
186            kind: self.kind,
187            xlora_order: self.xlora_order,
188            no_kv_cache: self.no_kv_cache,
189            chat_template: self.chat_template,
190            tokenizer_json: self.tokenizer_json,
191            tgt_non_granular_index: self.tgt_non_granular_index,
192            quantized_filename: Some(self.quantized_filename),
193            quantized_model_id: Some(self.quantized_model_id),
194            jinja_explicit: self.jinja_explicit,
195            lora_adapter_ids: None,
196        })
197    }
198}
199
200impl GGMLLoader {
201    #[allow(clippy::too_many_arguments)]
202    pub fn new(
203        model_id: Option<String>,
204        config: GGMLSpecificConfig,
205        quantized_model_id: Option<String>,
206        quantized_filename: Option<String>,
207        xlora_model_id: Option<String>,
208        kind: ModelKind,
209        xlora_order: Option<Ordering>,
210        no_kv_cache: bool,
211        chat_template: Option<String>,
212        tokenizer_json: Option<String>,
213        tgt_non_granular_index: Option<usize>,
214        jinja_explicit: Option<String>,
215    ) -> Self {
216        let model_id = if let Some(id) = model_id {
217            id
218        } else {
219            info!(
220                "Using adapter base model ID: `{}`",
221                xlora_order.as_ref().unwrap().base_model_id
222            );
223            xlora_order.as_ref().unwrap().base_model_id.clone()
224        };
225        Self {
226            model_id,
227            config,
228            quantized_model_id,
229            quantized_filename,
230            xlora_model_id,
231            xlora_order,
232            no_kv_cache,
233            chat_template,
234            tokenizer_json,
235            kind,
236            tgt_non_granular_index,
237            jinja_explicit,
238            lora_adapter_ids: None,
239        }
240    }
241}
242
243impl Loader for GGMLLoader {
244    #[allow(clippy::type_complexity, clippy::too_many_arguments)]
245    fn load_model_from_path(
246        &self,
247        paths: &Box<dyn ModelPaths>,
248        dtype: &dyn TryIntoDType,
249        device: &Device,
250        silent: bool,
251        mapper: DeviceMapSetting,
252        in_situ_quant: Option<IsqType>,
253        mut paged_attn_config: Option<PagedAttentionConfig>,
254    ) -> Result<Arc<Mutex<dyn Pipeline + Send + Sync>>> {
255        if in_situ_quant.is_some() {
256            anyhow::bail!(
257                "You are trying to in-situ quantize a GGML model. This will not do anything."
258            );
259        }
260
261        if matches!(mapper, DeviceMapSetting::Map(_)) {
262            anyhow::bail!("Device mapping is not supported for diffusion models.")
263        }
264
265        if paged_attn_config.is_some() {
266            warn!("PagedAttention is not supported for GGML models, disabling it.");
267
268            paged_attn_config = None;
269        }
270
271        // Apply default prompt size here
272        let prompt_chunksize = self
273            .config
274            .prompt_chunksize
275            .unwrap_or(DEFAULT_PROMPT_CHUNK_SIZE.try_into().unwrap())
276            .get();
277
278        info!("Prompt chunk size is {prompt_chunksize}.",);
279
280        info!(
281            "Loading model `{}` on {}.",
282            self.get_id(),
283            device.device_pretty_repr()
284        );
285
286        let mut file = std::fs::File::open(paths.get_weight_filenames().first().unwrap())?;
287        let model = ggml_file::Content::read(&mut file, device)
288            .map_err(|e| e.with_path(paths.get_weight_filenames().first().unwrap()))?;
289
290        info!("Model config: {:?}", model.hparams);
291
292        if DEBUG.load(std::sync::atomic::Ordering::Relaxed) {
293            let mut tensors = Vec::new();
294            for (name, t) in &model.tensors {
295                tensors.push(format!(
296                    "name = `{name}`, shape = {:?}, dtype = {:?}",
297                    t.shape().clone(),
298                    t.dtype(),
299                ));
300            }
301            fs::write(
302                "mistralrs_ggml_tensors.txt",
303                serde_json::to_string_pretty(&tensors).expect("Serialization failed."),
304            )?;
305
306            info!("Debug is enabled, wrote the names and information about each tensor to `mistralrs_ggml_tensors.txt`.");
307        }
308
309        let _ = if paged_attn_config.is_none() {
310            warn!("GGML does not currently support PagedAttention, running without");
311            None
312        } else {
313            paged_attn_config
314        };
315
316        let has_adapter = self.kind.is_adapted();
317        let is_xlora = self.kind.is_adapted_and(|a| a.is_x_lora());
318        let internal_dtype = dtype.try_into_dtype(&[device]).unwrap();
319
320        let model_config = {
321            // Base config (quantization only):
322            let quant = ModelConfig::ParamsGGML((model, self.config.gqa, internal_dtype).into());
323
324            // With optional adapter config:
325            let mut adapter = None;
326            if has_adapter {
327                adapter.replace(ModelConfig::Adapter::try_new(
328                    paths, device, silent, is_xlora,
329                )?);
330            }
331
332            ModelConfig::ModelParams::new(quant, adapter)
333        };
334
335        // Config into model:
336        // NOTE: No architecture to infer like GGUF, Llama model is implicitly matched
337        let model = match self.kind {
338            ModelKind::GgufQuantized { .. } => Model::Llama(QLlama::try_from(model_config)?),
339            ModelKind::GgufAdapter { .. } => {
340                Model::XLoraLlama(Box::new(XLoraQLlama::try_from(model_config)?))
341            }
342            _ => unreachable!(),
343        };
344
345        let tokenizer = get_tokenizer(paths.get_tokenizer_filename(), None)?;
346        let gen_conf: Option<GenerationConfig> = paths
347            .get_gen_conf_filename()
348            .map(|f| serde_json::from_str(&fs::read_to_string(f).unwrap()).unwrap());
349        let chat_template_explicit = paths
350            .get_chat_template_explicit()
351            .as_ref()
352            .map(|x| x.to_string_lossy().to_string());
353        let chat_template = get_chat_template(
354            paths,
355            self.jinja_explicit.as_ref(),
356            chat_template_explicit.as_ref(),
357            self.chat_template.as_ref(),
358            None,
359        );
360
361        let max_seq_len = match model {
362            Model::Llama(ref l) => l.max_seq_len,
363            Model::XLoraLlama(ref xl) => xl.max_seq_len,
364        };
365        let llg_factory = build_llg_factory(tokenizer.clone())?;
366        let num_hidden_layers = match model {
367            Model::Llama(ref model) => model.cache.normal().0.len(),
368            Model::XLoraLlama(ref model) => model.cache.full().lock().len(),
369        };
370        let eos = calculate_eos_tokens(&chat_template, gen_conf, &tokenizer);
371        Ok(Arc::new(Mutex::new(GGMLPipeline {
372            model,
373            tokenizer: tokenizer.into(),
374            no_kv_cache: self.no_kv_cache,
375            chat_template: Arc::new(chat_template),
376            model_id: self.model_id.clone(),
377            non_granular_state: self.tgt_non_granular_index.map(|tgt_non_granular_index| {
378                NonGranularState {
379                    non_granular_index: Arc::new(Mutex::new(0)),
380                    tgt_non_granular_index,
381                }
382            }),
383            metadata: Arc::new(GeneralMetadata {
384                max_seq_len,
385                llg_factory: Some(llg_factory),
386                no_kv_cache: self.no_kv_cache,
387                no_prefix_cache: false,
388                num_hidden_layers,
389                eos_tok: eos,
390                kind: self.kind.clone(),
391                is_xlora,
392                activation_dtype: internal_dtype,
393                sliding_window: None,
394                cache_config: None,
395                cache_engine: None,
396                prompt_chunksize: Some(NonZero::new(prompt_chunksize).unwrap()),
397                model_metadata: None,
398                modalities: Modalities {
399                    input: vec![SupportedModality::Text],
400                    output: vec![SupportedModality::Text],
401                },
402            }),
403        })))
404    }
405
406    #[allow(clippy::type_complexity, clippy::too_many_arguments)]
407    fn load_model_from_hf(
408        &self,
409        revision: Option<String>,
410        token_source: TokenSource,
411        dtype: &dyn TryIntoDType,
412        device: &Device,
413        silent: bool,
414        mapper: DeviceMapSetting,
415        in_situ_quant: Option<IsqType>,
416        paged_attn_config: Option<PagedAttentionConfig>,
417    ) -> Result<Arc<Mutex<dyn Pipeline + Send + Sync>>> {
418        let paths: anyhow::Result<Box<dyn ModelPaths>> = get_paths!(
419            LocalModelPaths,
420            &token_source,
421            revision,
422            self,
423            self.quantized_model_id,
424            Some(vec![self.quantized_filename.as_ref().unwrap().clone()]),
425            silent,
426            false // Never loading UQFF
427        );
428        self.load_model_from_path(
429            &paths?,
430            dtype,
431            device,
432            silent,
433            mapper,
434            in_situ_quant,
435            paged_attn_config,
436        )
437    }
438
439    fn get_id(&self) -> String {
440        self.xlora_model_id
441            .as_deref()
442            .unwrap_or(&self.model_id)
443            .to_string()
444    }
445
446    fn get_kind(&self) -> ModelKind {
447        self.kind.clone()
448    }
449}
450
451impl PreProcessingMixin for GGMLPipeline {
452    fn get_chat_template(&self) -> Option<Arc<ChatTemplate>> {
453        Some(self.chat_template.clone())
454    }
455    fn get_input_processor_config(&self) -> Option<Arc<dyn Any>> {
456        None
457    }
458}
459
460impl IsqPipelineMixin for GGMLPipeline {
461    fn re_isq_model(&mut self, _dtype: IsqType) -> Result<()> {
462        anyhow::bail!(
463            "You are trying to in-situ requantize a GGML model. This will not do anything."
464        )
465    }
466}
467
468impl CacheManagerMixin for GGMLPipeline {
469    fn clone_in_cache(&self, seqs: &mut [&mut Sequence]) {
470        FullCacheManager.clone_in_cache(self, seqs, false)
471    }
472    fn clone_out_cache(&self, seqs: &mut [&mut Sequence]) {
473        FullCacheManager.clone_out_cache(self, seqs, false)
474    }
475    fn set_none_cache(
476        &self,
477        seqs: &mut [&mut Sequence],
478        reset_non_granular: bool,
479        modify_draft_cache: bool,
480
481        load_preallocated_cache: bool,
482    ) {
483        FullCacheManager.set_none_cache(self, seqs, modify_draft_cache, load_preallocated_cache);
484        if reset_non_granular {
485            self.reset_non_granular_state()
486        }
487    }
488    fn cache(&self) -> &EitherCache {
489        match self.model {
490            Model::Llama(ref model) => &model.cache,
491            Model::XLoraLlama(ref model) => &model.cache,
492        }
493    }
494}
495
496impl MetadataMixin for GGMLPipeline {
497    fn device(&self) -> Device {
498        match self.model {
499            Model::Llama(ref model) => model.device.clone(),
500            Model::XLoraLlama(ref model) => model.device.clone(),
501        }
502    }
503    fn tokenizer(&self) -> Option<Arc<Tokenizer>> {
504        Some(self.tokenizer.clone())
505    }
506    fn name(&self) -> String {
507        self.model_id.clone()
508    }
509    fn reset_non_granular_state(&self) {
510        if let Some(s) = self.non_granular_state.as_ref() {
511            *self.cache().full().get_scalings_cache() = None;
512            *get_mut_arcmutex!(s.non_granular_index) = 0;
513        }
514    }
515    fn get_metadata(&self) -> Arc<GeneralMetadata> {
516        self.metadata.clone()
517    }
518    fn device_mapper(&self) -> Option<&dyn DeviceMapper> {
519        None
520    }
521}
522
523#[async_trait::async_trait]
524impl Pipeline for GGMLPipeline {
525    fn forward_inputs(
526        &mut self,
527        inputs: Box<dyn Any>,
528        return_raw_logits: bool,
529    ) -> Result<ForwardInputsResult, candle_core::Error> {
530        let ModelInputs {
531            input_ids,
532            input_ids_full,
533            seqlen_offsets,
534            seqlen_offsets_full,
535            context_lens,
536            position_ids: _,    // NOTE(EricLBuehler): ignore, it is for phi3
537            paged_attn_meta: _, // NOTE(EricLBuehler): ignore it for ggml
538            flash_meta,         // NOTE(EricLBuehler): ignore it for ggml dequant into f32
539            flash_meta_full,    // NOTE(EricLBuehler): ignore it for ggml dequant into f32
540        } = *inputs.downcast().expect("Downcast failed.");
541        let logits = match self.model {
542            Model::Llama(ref model) => {
543                model.forward(&input_ids, &seqlen_offsets, context_lens, None)?
544            }
545            Model::XLoraLlama(ref model) => model.forward(
546                &input_ids,
547                input_ids_full.as_ref().unwrap_or(&input_ids),
548                &seqlen_offsets,
549                seqlen_offsets_full.as_ref().unwrap_or(&seqlen_offsets),
550                self.no_kv_cache,
551                &self.non_granular_state,
552                context_lens,
553                &flash_meta,
554                flash_meta_full.as_ref().unwrap_or(&flash_meta),
555            )?,
556        };
557        if return_raw_logits {
558            Ok(ForwardInputsResult::RawLogits { logits })
559        } else {
560            Ok(ForwardInputsResult::CausalGeneration { logits })
561        }
562    }
563    async fn sample_causal_gen(
564        &self,
565        seqs: &mut [&mut Sequence],
566        logits: Vec<Tensor>,
567        prefix_cacher: &mut PrefixCacheManagerV2,
568        disable_eos_stop: bool,
569        rng: Arc<std::sync::Mutex<Isaac64Rng>>,
570    ) -> Result<(), candle_core::Error> {
571        sample_and_add_toks(self, seqs, logits, prefix_cacher, disable_eos_stop, rng).await
572    }
573    fn category(&self) -> ModelCategory {
574        ModelCategory::Text
575    }
576}
577
578// TODO
579impl AnyMoePipelineMixin for GGMLPipeline {}
mistralrs_core/pipeline/ggml.rs

mistralrs_core/pipeline/
ggml.rs
