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Merge pull request #9 from TheSeamau5/mistral
Add support for Mistral
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -5,4 +5,5 @@ Cargo.lock | |
| *.st | ||
| *.npx | ||
| *.npz | ||
| /**/llama-7b-hf | ||
| /**/llama-7b-hf | ||
| /**/mistral-7b-hf | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,181 @@ | ||
| use std::{marker::PhantomData, time::Instant}; | ||
|
|
||
| use colored::Colorize; | ||
| use rust_tokenizers::tokenizer::{SentencePieceBpeTokenizer, Tokenizer, TruncationStrategy}; | ||
| mod model; | ||
|
|
||
| use luminal::{prelude::*, shape::symbolic::Expression}; | ||
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| use crate::model::KVCache; | ||
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| #[cfg(feature = "metal")] | ||
| type DeviceCompiler = MetalFp16Compiler; | ||
| #[cfg(feature = "cuda")] | ||
| type DeviceCompiler = CudaFp16Compiler; | ||
| #[cfg(all(not(feature = "cuda"), not(feature = "metal")))] | ||
| type DeviceCompiler = CPUCompiler; | ||
|
|
||
| fn main() -> Result<(), String> { | ||
| println!("Constructing graph..."); | ||
| let tokenizer = SentencePieceBpeTokenizer::from_file( | ||
| "./examples/mistral/setup/mistral-7b-hf/tokenizer.model", | ||
| false, | ||
| ) | ||
| .unwrap(); | ||
|
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| let mut cx1 = Graph::new(); | ||
| let input = cx1.named_tensor::<(Const<1>, Dyn<'s'>)>("Input"); | ||
| let model = model::MistralLM::initialize(&mut cx1); | ||
| let (logits, kv_cache) = model.forward(( | ||
| input, | ||
| Option::<Vec<KVCache<Const<1>, Const<0>>>>::None, | ||
| PhantomData::<Dyn<'s'>>, | ||
| )); | ||
| let logits = logits | ||
| .slice((.., (Expression::from('s') - 1).., ..)) | ||
| .retrieve(); | ||
| kv_cache.keep(); | ||
| SafeTensorLoader::new(vec![ | ||
| "./examples/mistral/setup/mistral-7b-hf/model-00001-of-00003.safetensors".to_string(), | ||
| "./examples/mistral/setup/mistral-7b-hf/model-00002-of-00003.safetensors".to_string(), | ||
| "./examples/mistral/setup/mistral-7b-hf/model-00003-of-00003.safetensors".to_string(), | ||
| ]) | ||
| .load(&model, &mut cx1); | ||
| let mut cx2 = Graph::new(); | ||
| let single_input = cx2.named_tensor::<R2<1, 1>>("Input"); | ||
| let kv_model = model::MistralLM::initialize(&mut cx2); | ||
| let cache_src: Vec<KVCache<Const<1>, Dyn<'p'>>> = (0..model::NUM_LAYERS) | ||
| .map(|_| { | ||
| ( | ||
| cx2.named_tensor("Key Cache"), | ||
| cx2.named_tensor("Value Cache"), | ||
| ) | ||
| }) | ||
| .collect(); | ||
| let (decode_logits, cache_dest) = kv_model.forward(( | ||
| single_input, | ||
| Some(cache_src.clone()), | ||
| PhantomData::<Dyn<'t'>>, | ||
| )); | ||
| decode_logits.retrieve(); | ||
| cache_dest.keep(); | ||
|
|
||
| println!("Compiling graph..."); | ||
| cx1.compile(GenericCompiler::<DeviceCompiler>::default()); | ||
| // Cache model weights | ||
| cx1.compile(RemapDownstream( | ||
| state_dict(&model).values().copied().collect(), | ||
| )); | ||
| keep_weights(&model, &mut cx1); | ||
|
|
||
| // Compile second graph | ||
| cx2.compile(GenericCompiler::<DeviceCompiler>::default()); | ||
| // Cache model weights | ||
| cx2.compile(RemapDownstream( | ||
| state_dict(&kv_model).values().copied().collect(), | ||
| )); | ||
| keep_weights(&kv_model, &mut cx2); | ||
| delete_inputs( | ||
| &state_dict(&kv_model).values().copied().collect::<Vec<_>>(), | ||
| &mut cx2, | ||
| ); | ||
| delete_inputs( | ||
| &cache_src | ||
| .iter() | ||
| .flat_map(|(k, v)| [k.id(), v.id()]) | ||
| .collect::<Vec<_>>(), | ||
| &mut cx2, | ||
| ); | ||
|
|
||
| // Initial forward pass to load weights | ||
| println!("Loading model..."); | ||
| input.set_dyn(vec![1.], vec![1, 1]); | ||
| cx1.execute(); | ||
| logits.drop(); | ||
| kv_cache.drop(); | ||
|
|
||
| // Now that weights are loaded, delete the loading nodes so they don't run again | ||
| delete_inputs( | ||
| &state_dict(&model).values().copied().collect::<Vec<_>>(), | ||
| &mut cx1, | ||
| ); | ||
|
|
||
| // Run inference first pass | ||
| let prompt = "Santa says: Merry"; | ||
| let mut input_ids = encode(&tokenizer, prompt); | ||
|
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| let mut completion = String::new(); | ||
| input.set_dyn( | ||
| input_ids.iter().map(|i| *i as f32).collect::<Vec<_>>(), | ||
| vec![1, input_ids.len()], | ||
| ); | ||
| cx1.execute(); | ||
|
|
||
| let output_id = sample_index(&logits.data()); | ||
| input_ids.push(output_id); | ||
|
|
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| // Decode token | ||
| completion.push_str(&decode(&tokenizer, &[output_id])); | ||
| println!("{}{}", prompt.on_black().white().bold(), completion.green()); | ||
|
|
||
| // Transfer weights and kv cache | ||
| transfer_weights(&model, &mut cx1, &kv_model, &mut cx2); | ||
| for ((key_src, val_src), (key_dest, val_dest)) in kv_cache.into_iter().zip(cache_src.iter()) { | ||
| cx2.set_tensor(key_dest.id(), 0, cx1.get_tensor(key_src.id(), 0).unwrap()); | ||
| cx2.set_tensor(val_dest.id(), 0, cx1.get_tensor(val_src.id(), 0).unwrap()); | ||
| } | ||
|
|
||
| // Decode loop | ||
| for _ in 0..100 { | ||
| single_input.set(vec![*input_ids.last().unwrap() as f32]); | ||
| cx2.set_dyn_dim('p', input_ids.len() - 1); | ||
| cx2.set_dyn_dim('t', input_ids.len()); | ||
|
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||
| let now = Instant::now(); | ||
| cx2.execute(); | ||
| println!("Forward Pass Took {:.2}s", now.elapsed().as_secs_f32()); | ||
|
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| // Sample tokens | ||
| let output_id = sample_index(&decode_logits.data()); | ||
| decode_logits.drop(); | ||
| completion.push_str(&decode(&tokenizer, &[output_id])); | ||
| input_ids.push(output_id); | ||
| println!("{}{}", prompt.on_black().white().bold(), completion.green()); | ||
|
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||
| // Swap caches | ||
| for ((src_k, src_v), (dest_k, dest_v)) in cache_src.iter().zip(cache_dest.iter()) { | ||
| // Move dest caches to src | ||
| cx2.swap_tensors(*src_k, *dest_k); | ||
| cx2.swap_tensors(*src_v, *dest_v); | ||
| // // Drop dest caches | ||
| // dest_k.drop(); | ||
| // dest_v.drop(); | ||
| } | ||
| } | ||
|
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| Ok(()) | ||
| } | ||
|
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| // Method to encode text as vector | ||
| pub fn encode(tokenizer: &SentencePieceBpeTokenizer, text: &str) -> Vec<i64> { | ||
| let mut vector = tokenizer | ||
| .encode(text, None, text.len(), &TruncationStrategy::LongestFirst, 0) | ||
| .token_ids; | ||
| vector.insert(0, 1); // Start token | ||
| vector | ||
| } | ||
|
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| pub fn decode(tokenizer: &SentencePieceBpeTokenizer, token_ids: &[i64]) -> String { | ||
| tokenizer | ||
| .decode(token_ids, true, false) | ||
| .replace("<0x0A>", "\n") | ||
| } | ||
|
|
||
| // Currently just an argmax, do actual sampling here | ||
| fn sample_index(dist: &[f32]) -> i64 { | ||
| dist.iter() | ||
| .enumerate() | ||
| .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)) | ||
| .unwrap() | ||
| .0 as i64 | ||
| } |
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