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First commit. LLaMA works now. It is not pretty but it does generate text from prompts. Yay.

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broken-opencl-code
Mikko Juola 3 years ago
commit 3b8f904f13
20 changed files with 8021 additions and 0 deletions
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Cargo.toml
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[package]
name = "rllama"
version = "0.1.0"
edition = "2021"
[lib]
path = "src/lib.rs"
[[bin]]
name = "rllama"
path = "src/main.rs"
[dependencies]
protobuf = "3.2"
thiserror = "1.0"
half = "2.2"
num-complex = "0.4"
embedded-profiling = "0.3"
rand = "0.8"
approx = "0.5"
rayon = "1.7"
clap = { version = "4.1", features = ["derive"] }
indicatif = "0.17"
# We need protobuf compiler
[build-dependencies]
protobuf-codegen = "3.2"
protobuf-parse = "3.2"
[dev-dependencies]
criterion = "0.4"
[profile.release]
debug = true
[[bench]]
path = "src/benches/benchmark.rs"
name = "benchmark"
harness = false

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LICENSE
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If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.

208
LICENSE.third_parties
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proto/ directory contains a protobuf file from Google's
https://github.com/google/sentencepiece repository.
Here is their license: (note rllama as a whole is AGPL3)
-----
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21
README.md
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# AdeonLLaMA
This is my attempt at making the LLaMA language model working on a pure Rust
CPU implementation.
As of writing of this, it can run LLaMA-7B at around ~1 token per second, using
something like 1.5 threads because I haven't yet properly figured out how to
multithread this.
It uses AVX2 intrinsics to speed up itself.
# How to run
You will need the LLaMA-7B weights first. Refer to https://github.com/facebookresearch/llama/
Once you have 7B weights, and the `tokenizer.model` it comes with, you can make
it generate tokens:
```shell
cargo run --release -- --tokenizer-model /path/to/tokenizer.model --model-path /path/to/LLaMA/7B
```

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build.rs
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fn main() {
protobuf_codegen::Codegen::new()
.pure()
.out_dir("src/protomodels")
.include("proto")
.input("proto/sentencepiece_model.proto")
.run()
.unwrap();
}

321
proto/sentencepiece_model.proto
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// Copyright 2016 Google Inc.
//
// 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.!
syntax = "proto2";
// TODO(taku): Needs to use LITE RUNTIME in OSS release.
option optimize_for = LITE_RUNTIME;
package sentencepiece;
// TrainerSpec encodes a various parameters for SentencePiece training.
// Next id: 53
message TrainerSpec {
///////////////////////////////////////////////////////////////////
// General parameters
//
// Input corpus files.
// Trainer accepts the following two formats:
// A) Monolingual: plain text, one sentence per line.
// B) Bilingual: TSV, source sentence <tab> target sentence
// When bilingual data is passed, shared vocabulary model is built.
// Note that the input file must be raw corpus, not a preprocessed corpus.
// Trainer only loads the first `input_sentence_size` sentences specified
// with this parameter.
repeated string input = 1;
// Input corpus format:
// "text": one-sentence-per-line text format (default)
// "tsv": sentence <tab> freq
optional string input_format = 7;
// Output model file prefix.
// <model_prefix>.model and <model_prefix>.vocab are generated.
optional string model_prefix = 2;
// Model type. only have UNIGRAM now.
enum ModelType {
UNIGRAM = 1; // Unigram language model with dynamic algorithm
BPE = 2; // Byte Pair Encoding
WORD = 3; // Delimitered by whitespace.
CHAR = 4; // tokenizes into character sequence
}
optional ModelType model_type = 3 [default = UNIGRAM];
// Vocabulary size. 8k is the default size.
optional int32 vocab_size = 4 [default = 8000];
// List of the languages this model can accept.
// Since the model is language-agnostic, this field is used as a reference.
repeated string accept_language = 5;
// Size of self-test samples, which are encoded in the model file.
optional int32 self_test_sample_size = 6 [default = 0];
// Whether to use DP version of sentencepiece. Use it with TSV input format
// (requires precomputed word tab counts to work).
optional bool enable_differential_privacy = 50 [default = false];
// Set these parameters if you need DP version of sentencepiece.
// std of noise to add.
optional float differential_privacy_noise_level = 51 [default = 0.0];
// Clipping threshold to apply after adding noise. All the words with
// frequency less than this value are dropped.
optional uint64 differential_privacy_clipping_threshold = 52 [default = 0];
///////////////////////////////////////////////////////////////////
// Training parameters.
//
// Uses characters which cover the corpus with the ratio of `chars_coverage`.
// This parameter determines the set of basic Alphabet of sentence piece.
// 1.0 - `chars_coverage` characters are treated as UNK.
// See also required_chars field.
optional float character_coverage = 10 [default = 0.9995];
// Maximum size of sentences the trainer loads from `input` parameter.
// Trainer simply loads the `input` files in sequence.
// It is better to shuffle the input corpus randomly.
optional uint64 input_sentence_size = 11 [default = 0];
optional bool shuffle_input_sentence = 19 [default = true];
// Maximum size of sentences to make seed sentence pieces.
// Extended suffix array is constructed to extract frequent
// sub-strings from the corpus. This uses 20N working space,
// where N is the size of corpus.
optional int32 mining_sentence_size = 12 [deprecated = true];
// Maximum size of sentences to train sentence pieces.
optional int32 training_sentence_size = 13 [deprecated = true];
// The size of seed sentencepieces.
// `seed_sentencepiece_size` must be larger than `vocab_size`.
optional int32 seed_sentencepiece_size = 14 [default = 1000000];
// In every EM sub-iterations, keeps top
// `shrinking_factor` * `current sentencepieces size` with respect to
// the loss of the sentence piece. This value should be smaller than 1.0.
optional float shrinking_factor = 15 [default = 0.75];
// The maximum sentence length in byte. The sentences with the length
// larger than `max_sentence_length` is simply ignored.
// Longer input tends to bring the following risks:
// * Overflow during EM training (unigram language model only)
// * Performance drop because of O(n log n) cost in BPE.
optional int32 max_sentence_length = 18 [default = 4192];
// Number of threads in the training.
optional int32 num_threads = 16 [default = 16];
// Number of EM sub iterations.
optional int32 num_sub_iterations = 17 [default = 2];
///////////////////////////////////////////////////////////////////
// SentencePiece parameters which control the shapes of sentence piece.
//
// Maximum length of sentencepiece.
optional int32 max_sentencepiece_length = 20 [default = 16];
// Uses Unicode script to split sentence pieces.
// When `split_by_unicode_script` is true, we do not allow sentence piece to
// include multiple Unicode scripts, e.g. "F1" is not a valid piece.
// Exception: CJ characters (Hiragana/Katakana/Han) are all handled
// as one script type, since Japanese word can consist of multiple scripts.
// This exception is always applied regardless of the accept-language
// parameter.
optional bool split_by_unicode_script = 21 [default = true];
// When `split_by_number` is true, put a boundary between number and
// non-number transition. If we want to treat "F1" is one token, set this flag
// to be false.
optional bool split_by_number = 23 [default = true];
// Use a white space to split sentence pieces.
// When `split_by_whitespace` is false, we may have the piece containing
// a white space in the middle. e.g., "in_the".
optional bool split_by_whitespace = 22 [default = true];
// Adds whitespace symbol (_) as a suffix instead of prefix. e.g., _hello =>
// hello_. When `treat_whitespace_as_suffix` is true,
// NormalizerSpec::add_dummy_prefix will add the dummy whitespace to the end
// of sentence.
optional bool treat_whitespace_as_suffix = 24 [default = false];
// Allows pieces that only contain whitespaces instead of appearing only as
// prefix or suffix of other pieces.
optional bool allow_whitespace_only_pieces = 26 [default = false];
// Split all digits (0-9) into separate pieces.
optional bool split_digits = 25 [default = false];
///////////////////////////////////////////////////////////////////
// Vocabulary management
//
// Defines control symbols used as an indicator to
// change the behavior of the decoder. <s> and </s> are pre-defined.
// We can use this field to encode various meta information,
// including language indicator in multilingual model.
// These symbols are not visible to users, but visible to
// the decoder. Note that when the input sentence contains control symbols,
// they are not treated as one token, but segmented into normal pieces.
// Control symbols must be inserted independently from the segmentation.
repeated string control_symbols = 30;
// Defines user defined symbols.
// These symbols are added with extremely high score
// so they are always treated as one unique symbol in any context.
// Typical usage of user_defined_symbols is placeholder for named entities.
repeated string user_defined_symbols = 31;
// Defines required characters. Each UTF8 character in this string is included
// in the character set regardless of character_coverage value. Unlike
// user_defined_symbols, these characters have scores based on the frequency
// on input sentences, and the model can form subwords using characters
// in this field.
optional string required_chars = 36;
// Decomposes unknown pieces into UTF-8 bytes.
optional bool byte_fallback = 35 [default = false];
// When creating the vocabulary file, defines whether or not to additionally
// output the score for each piece.
optional bool vocabulary_output_piece_score = 32 [default = true];
// `vocab_size` is treated as hard limit. Crash if
// the model can not produce the vocab of size `vocab_size`,
// When `hard_vocab_limit` is false, vocab_size is treated
// as soft limit. Note that when model_type=char,
// always assumes hard_vocab_limit = false.
optional bool hard_vocab_limit = 33 [default = true];
// use all symbols for vocab extraction. This flag is valid
// if model type is either CHAR or WORD
optional bool use_all_vocab = 34 [default = false];
///////////////////////////////////////////////////////////////////
// Reserved special meta tokens.
// * -1 is not used.
// * unk_id must not be -1.
// Id must starts with 0 and be contigous.
optional int32 unk_id = 40 [default = 0]; // <unk>
optional int32 bos_id = 41 [default = 1]; // <s>
optional int32 eos_id = 42 [default = 2]; // </s>
optional int32 pad_id = 43 [default = -1]; // <pad> (padding)
optional string unk_piece = 45 [default = "<unk>"];
optional string bos_piece = 46 [default = "<s>"];
optional string eos_piece = 47 [default = "</s>"];
optional string pad_piece = 48 [default = "<pad>"];
// Encodes <unk> into U+2047 (DOUBLE QUESTION MARK),
// since this character can be useful both for user and
// developer. We can easily figure out that <unk> is emitted.
optional string unk_surface = 44 [default = " \xE2\x81\x87 "];
// Increase bit depth to allow unigram model training on large
// (>10M sentences) corpora. A Side-effect of enabling this flag
// is increased memory usage.
optional bool train_extremely_large_corpus = 49 [default = false];
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
// NormalizerSpec encodes a various parameters for string normalizaiton
message NormalizerSpec {
// name of normalization rule.
optional string name = 1;
// Pre-compiled normalization rule created by
// Builder::GetPrecompiledCharsMap() or Builder::CompileCharsMap() method.
// Usually this field is set by Builder::GetNormalizerSpec() method.
optional bytes precompiled_charsmap = 2;
// Adds dummy whitespace at the beginning of text in order to
// treat "world" in "world" and "hello world" in the same way.
optional bool add_dummy_prefix = 3 [default = true];
// Removes leading, trailing, and duplicate internal whitespace.
optional bool remove_extra_whitespaces = 4 [default = true];
// Replaces whitespace with meta symbol.
// This field must be true to train sentence piece model.
optional bool escape_whitespaces = 5 [default = true];
// Custom normalization rule file in TSV format.
// https://github.com/google/sentencepiece/blob/master/doc/normalization.md
// This field is only used in SentencePieceTrainer::Train() method, which
// compiles the rule into the binary rule stored in `precompiled_charsmap`.
optional string normalization_rule_tsv = 6;
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
// Proto to store samples for self-testing.
message SelfTestData {
message Sample {
optional string input = 1;
optional string expected = 2;
}
repeated Sample samples = 1;
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
// ModelProto stores model parameters.
// SentencePieceProcessor is supposed to be self-contained.
// All settings/parameters which may change the behavior must be encoded
// in ModelProto.
message ModelProto {
message SentencePiece {
enum Type {
NORMAL = 1; // normal symbol
UNKNOWN = 2; // unknown symbol. only <unk> for now.
CONTROL = 3; // control symbols. </s>, <s>, <2ja> etc.
USER_DEFINED = 4; // user defined symbols.
// Typical usage of USER_DEFINED symbol
// is placeholder.
BYTE = 6; // byte symbols. Used when `byte_fallback` is true.
UNUSED = 5; // this piece is not used.
}
optional string piece = 1; // piece must not be empty.
optional float score = 2;
optional Type type = 3 [default = NORMAL];
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
// Sentence pieces with scores.
repeated SentencePiece pieces = 1;
// Spec used to generate this model file.
optional TrainerSpec trainer_spec = 2;
// Spec for text normalization.
optional NormalizerSpec normalizer_spec = 3;
// Stores sample input and its expected segmentation to verify the model.
optional SelfTestData self_test_data = 4;
// Spec for text de-normalization.
optional NormalizerSpec denormalizer_spec = 5;
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}

85
src/benches/benchmark.rs
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extern crate rllama;
use rllama::tensor::{Tensor, TensorDType};
use criterion::{black_box, criterion_group, criterion_main, Criterion};
pub fn tensor_benchmarks(c: &mut Criterion) {
let orig16_1 = Tensor::full(16, 32, TensorDType::Float16, 3.0);
let orig16_2 = Tensor::full(32, 512, TensorDType::Float16, -1.33);
let orig32_1 = Tensor::full(16, 32, TensorDType::Float32, 3.0);
let orig32_2 = Tensor::full(32, 512, TensorDType::Float32, -1.33);
let orig32_2_transposed = orig32_2.transpose();
let mut result_16 = Tensor::zeros(16, 512, TensorDType::Float16);
let mut result_32 = Tensor::zeros(16, 512, TensorDType::Float32);
let orig_84096_1 = Tensor::zeros(8, 4096, TensorDType::Float32);
let orig_84096_2 = Tensor::zeros(4096, 4096, TensorDType::Float32);
let mut result_84096 = Tensor::zeros(8, 4096, TensorDType::Float32);
c.bench_function(
"matrix multiplication 8x4096 @ 4096x4096 f32 in-place",
|b| {
b.iter(|| {
let _ = result_84096
.matrix_mul_inplace(black_box(&orig_84096_1), black_box(&orig_84096_2));
})
},
);
c.bench_function(
"matrix multiplication 8x4096 @ 4096x4096 f32 in-place, transposed",
|b| {
b.iter(|| {
let _ = result_84096.matrix_mul_inplace_transposed(
black_box(&orig_84096_1),
black_box(&orig_84096_2),
);
})
},
);
c.bench_function("matrix multiplication f32 not in-place", |b| {
b.iter(|| {
let _ = black_box(&orig32_1).matrix_mul(black_box(&orig32_2));
})
});
c.bench_function("matrix multiplication f32 naive", |b| {
b.iter(|| {
let _ = black_box(&orig32_1).matrix_mul_naive(black_box(&orig32_2));
})
});
c.bench_function("matrix multiplication f16 not in-place", |b| {
b.iter(|| {
let _ = black_box(&orig16_1).matrix_mul(black_box(&orig16_2));
})
});
c.bench_function("matrix multiplication f16 naive", |b| {
b.iter(|| {
let _ = black_box(&orig16_1).matrix_mul_naive(black_box(&orig16_2));
})
});
c.bench_function("matrix multiplication f16 in-place", |b| {
b.iter(|| {
let _ = result_16.matrix_mul_inplace(black_box(&orig16_1), black_box(&orig16_2));
})
});
c.bench_function("matrix multiplication f32 in-place", |b| {
b.iter(|| {
let _ = result_32.matrix_mul_inplace(black_box(&orig32_1), black_box(&orig32_2));
})
});
c.bench_function("matrix multiplication f32 in-place, transposed", |b| {
b.iter(|| {
let _ = result_32.matrix_mul_inplace_transposed(
black_box(&orig32_1),
black_box(&orig32_2_transposed),
);
})
});
}
criterion_group!(benches, tensor_benchmarks);
criterion_main!(benches);

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src/embedding.rs
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use crate::tensor::Tensor;
use crate::unpickler;
use crate::unpickler::*;
use std::collections::BTreeMap;
use std::path::Path;
pub struct Embedding {
wgts: BTreeMap<usize, Tensor>,
}
impl Embedding {
pub fn from_unpickled<P: AsRef<Path>>(
unpickled: &unpickler::Value,
data_dir: P,
) -> Result<Self, UnpicklingError> {
let data_dir: &Path = data_dir.as_ref();
let val = match unpickled.get_str_key("tok_embeddings.weight") {
Some(val) => val,
None => {
return Err(UnpicklingError::MissingField(
"tok_embeddings.weight".to_string(),
))
}
};
let tensor = val
.to_tensor_builder()
.ok_or(UnpicklingError::InvalidTensorData)?;
let tensor = tensor.load(data_dir)?;
let num_embeddings = tensor.rows();
let mut table: BTreeMap<usize, Tensor> = BTreeMap::new();
for key in 0..num_embeddings {
let row = tensor.row(key);
table.insert(key as usize, row);
}
Ok(Self { wgts: table })
}
pub fn get_embedding(&self, idx: usize) -> &Tensor {
self.wgts.get(&idx).unwrap()
}
}

10
src/lib.rs
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#![feature(stdsimd)]
pub mod embedding;
pub mod protomodels;
pub mod rllama_main;
pub mod tensor;
pub mod token_sampler;
pub mod tokenizer;
pub mod transformer;
pub mod unpickler;

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src/main.rs
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pub fn main() -> Result<(), Box<dyn std::error::Error>> {
rllama::rllama_main::main()
}

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src/protomodels/mod.rs
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// @generated
pub mod sentencepiece_model;

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src/protomodels/sentencepiece_model.rs
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src/rllama_main.rs
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use crate::embedding::Embedding;
use crate::token_sampler::TokenSampler;
use crate::tokenizer::{TokenId, Tokenizer};
use crate::transformer::Transformer;
use crate::unpickler;
use clap::Parser;
use std::io::Read;
#[derive(Parser)]
#[command(author, version, about, long_about = None)]
struct Cli {
#[arg(long)]
model_path: String,
#[arg(long)]
tokenizer_path: String,
#[arg(long)]
prompt: String,
#[arg(long)]
temperature: Option<f32>,
#[arg(long)]
top_p: Option<f32>,
#[arg(long)]
top_k: Option<i32>,
}
pub fn main() -> Result<(), Box<dyn std::error::Error>> {
let cli = Cli::parse();
let model_path = cli.model_path;
let tokenizer_path = cli.tokenizer_path;
let prompt = cli.prompt;
println!("Starting up. Loading tokenizer from {}...", tokenizer_path);
let tok = Tokenizer::load(tokenizer_path.as_str())?;
println!("Tokenizer loeaded. Loading model from {}...", model_path);
let mut fs = std::fs::File::open(model_path.as_str())?;
let mut bs = Vec::new();
fs.read_to_end(&mut bs)?;
std::mem::drop(fs);
// We chop off file name from model_path and append "data/"
let model_data_dir = model_path
.split("/")
.take(model_path.split("/").count() - 1)
.collect::<Vec<&str>>()
.join("/")
+ "/data/";
let result = unpickler::unpickle(&bs)?;
println!("Loading embeddings from {}...", model_data_dir);
let emb = Embedding::from_unpickled(&result, model_data_dir.clone())?;
println!("Loading transformer weights from {}...", model_data_dir);
let tr = Transformer::from_unpickled(
&result,
emb,
4096,
32,
32,
512,
1e-6,
32,
128,
model_data_dir,
)?;
println!("All is loaded. Starting inference.");
let mut toks_id: Vec<TokenId> = tok.tokenize_to_ids(prompt);
let mut prev_pos = 0;
let mut token_sampler = TokenSampler::new().temperature(0.8).top_p(0.9).top_k(50);
if let Some(temperature) = cli.temperature {
token_sampler = token_sampler.temperature(temperature);
}
if let Some(top_p) = cli.top_p {
token_sampler = token_sampler.top_p(top_p);
}
if let Some(top_k) = cli.top_k {
token_sampler = token_sampler.top_k(top_k as usize);
}
println!("Temperature: {}", token_sampler.get_temperature());
println!("Top P: {}", token_sampler.get_top_p());
println!("Top K: {}", token_sampler.get_top_k());
let mut caches = tr.make_caches();
loop {
let preds = tr.forward(&toks_id[prev_pos..], prev_pos, &mut caches);
let highest_pred_idx = token_sampler.sample(&preds);
toks_id.push(highest_pred_idx as TokenId);
prev_pos = toks_id.len() - 1;
let mut tok_str: String = "".to_string();
for tok_id in toks_id.iter() {
if *tok_id == 1 {
continue;
}
let tok = tok.id_to_str(*tok_id);
tok_str = tok_str + tok.replace("▁", " ").as_str();
}
println!("{}", tok_str);
}
}

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src/tensor.rs
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src/token_sampler.rs
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use crate::tensor::Tensor;
use crate::tokenizer::TokenId;
use rand::Rng;
pub struct TokenSampler {
temperature: f32,
top_p: f32,
top_k: usize,
}
impl TokenSampler {
pub fn new() -> Self {
Self {
temperature: 0.8,
top_p: 1.0,
top_k: 1, // same as argmax
}
}
pub fn get_temperature(&self) -> f32 {
self.temperature
}
pub fn get_top_p(&self) -> f32 {
self.top_p
}
pub fn get_top_k(&self) -> usize {
self.top_k
}
pub fn temperature(self, temperature: f32) -> Self {
Self {
temperature,
..self
}
}
pub fn top_p(self, top_p: f32) -> Self {
Self { top_p, ..self }
}
pub fn top_k(self, top_k: usize) -> Self {
Self { top_k, ..self }
}
pub fn sample(&self, logits: &Tensor) -> TokenId {
let nrows = logits.rows();
assert!(logits.cols() == 1);
let mut logits = logits.transpose();
if self.temperature > 0.0 {
logits = logits.scalar_multiply_f32(1.0 / self.temperature);
logits = logits.softmax();
}
let mut logitsf: Vec<(TokenId, f32)> = Vec::with_capacity(nrows as usize);
for i in 0..nrows {
logitsf.push((i as TokenId, logits.get_f32(0, i)));
}
logitsf.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
logitsf.truncate(self.top_k as usize);
let mut p_accum: f32 = 0.0;
for (idx, v) in logitsf.iter().enumerate() {
p_accum += v.1;
if p_accum >= self.top_p {
logitsf.truncate(idx + 1);
break;
}
}
let mut total_p: f32 = 0.0;
for v in logitsf.iter() {
total_p += v.1;
}
let mut rng = rand::thread_rng();
let p: f32 = rng.gen_range(0.0..total_p);
p_accum = 0.0;
for v in logitsf.into_iter() {
p_accum += v.1;
if p_accum >= p {
return v.0;
}
}
0
}
}

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src/tokenizer.rs
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use crate::protomodels::sentencepiece_model::model_proto::sentence_piece;
use crate::protomodels::sentencepiece_model::ModelProto;
use protobuf::Message;
use std::collections::BTreeMap;
use std::io::Read;
use std::path::Path;
use thiserror::Error;
pub type TokenId = i32;
#[derive(Clone, Debug)]
pub struct Tokenizer {
pieces: BTreeMap<String, Piece>,
}
#[derive(Clone, Debug, Copy, Eq, Ord, PartialEq, PartialOrd)]
pub enum PieceType {
Normal,
Unknown,
Control,
UserDefined,
Byte,
Unused,
}
#[derive(Clone, Debug)]
pub struct Piece {
_tp: PieceType,
// piece: String this is in the BTreeMap that holds the pieces
_score: f32,
idx: usize,
}
#[derive(Error, Debug)]
pub enum TokenizerError {
#[error("IO error")]
IoError(#[from] std::io::Error),
#[error("Protobuf error")]
ProtobufError(#[from] protobuf::Error),
#[error("Unknown piece type")]
UnknownPieceType(String),
}
impl Tokenizer {
pub fn load<P: AsRef<Path>>(path: P) -> Result<Tokenizer, TokenizerError> {
let mut fs = std::fs::File::open(path)?;
let mut buffer = Vec::new();
fs.read_to_end(&mut buffer)?;
std::mem::drop(fs);
let model = ModelProto::parse_from_bytes(&buffer)?;
let mut pieces = BTreeMap::new();
for (idx, piece) in model.pieces.iter().enumerate() {
let piece_str = piece.piece.clone();
if piece_str.is_none() {
continue;
}
let piece_str = piece_str.unwrap();
let piece_type = match piece.type_ {
None => sentence_piece::Type::NORMAL,
Some(v) => match v.enum_value() {
Err(_) => return Err(TokenizerError::UnknownPieceType(piece_str)),
Ok(v) => v,
},
};
let score = piece.score.unwrap_or(0.0);
let tp = if piece_type == sentence_piece::Type::NORMAL {
PieceType::Normal
} else if piece_type == sentence_piece::Type::UNKNOWN {
PieceType::Unknown
} else if piece_type == sentence_piece::Type::CONTROL {
PieceType::Control
} else if piece_type == sentence_piece::Type::USER_DEFINED {
PieceType::UserDefined
} else if piece_type == sentence_piece::Type::BYTE {
PieceType::Byte
} else if piece_type == sentence_piece::Type::UNUSED {
PieceType::Unused
} else {
return Err(TokenizerError::UnknownPieceType(piece_str));
};
pieces.insert(
piece_str,
Piece {
_tp: tp,
_score: score,
idx,
},
);
}
Ok(Tokenizer { pieces })
}
// Gives a string for a token id.
// Panics if the id is out of range.
pub fn id_to_str(&self, id: i32) -> &str {
let id = id as usize;
for (piece_str, piece_info) in self.pieces.iter() {
if piece_info.idx == id {
return piece_str;
}
}
panic!("id out of range");
}
// Converts a string to a Vec<&str>
// You may want to use tokenize_to_ids instead.
//
// This will not add start or end tokens; only the string is processed.
//
// I noticed LLaMa code adds an extra space character at the beginning of the string, this
// function does not do that either.
pub fn tokenize_to_pieces<S: AsRef<str>>(&self, s: S) -> Vec<&str> {
let mut s: &str = s.as_ref();
let mut result: Vec<&str> = Vec::new();
// Very naive matching
while !s.is_empty() {
let mut best_candidate: &str = "";
let mut best_candidate_len: usize = 0;
let mut skip_s: &str = "";
for (piece_str, _piece_info) in self.pieces.iter() {
if s.starts_with(piece_str) && best_candidate_len < piece_str.len() {
best_candidate = piece_str;
best_candidate_len = piece_str.len();
skip_s = &s[piece_str.len()..];
}
}
if best_candidate_len == 0 {
// Skip token.
s = s.get(1..).unwrap_or("");
} else {
result.push(best_candidate);
s = skip_s;
}
}
result
}
pub fn tokenize_to_ids<S: AsRef<str>>(&self, s: S) -> Vec<TokenId> {
let mut s: String = format!("▁{}", s.as_ref());
// Replace all space characters with a special token.
s = s.replace(" ", "▁");
let pieces = self.tokenize_to_pieces(s);
let mut result = Vec::new();
result.push(1); // start token
for piece in pieces {
let piece_info = self.pieces.get(piece).unwrap();
result.push(piece_info.idx as i32);
}
result
}
}

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src/transformer.rs
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use crate::embedding::Embedding;
use crate::tensor::{Tensor, TensorDType};
use crate::tokenizer::TokenId;
use crate::unpickler;
use crate::unpickler::UnpicklingError;
use indicatif::ProgressBar;
use num_complex::Complex;
use rayon::prelude::*;
use std::path::Path;
use std::sync::{Arc, RwLock};
type FreqsCis = Vec<Vec<Complex<f64>>>;
#[allow(dead_code)]
pub struct Transformer {
freqs_cis: FreqsCis,
emb: Embedding,
dim: usize,
n_layers: usize,
n_heads: usize,
n_local_heads: usize,
max_seq_len: usize,
head_dim: usize,
norm: RMSNorm,
output: Tensor,
layers: Vec<TransformerBlock>,
}
pub struct TransformerCaches {
layer_caches: Vec<AttentionCache>,
}
pub struct TransformerBlock {
feed_forward: FeedForward,
attn: Attention,
ffn_norm: RMSNorm,
attention_norm: RMSNorm,
}
pub struct AttentionCache {
cache_k: Vec<Arc<RwLock<Tensor>>>,
cache_v: Vec<Arc<RwLock<Tensor>>>,
}
impl AttentionCache {
fn new(max_seq_len: usize, n_local_heads: usize, head_dim: usize) -> Self {
let mut cache_k = Vec::with_capacity(n_local_heads);
let mut cache_v = Vec::with_capacity(n_local_heads);
for _ in 0..n_local_heads {
cache_k.push(Arc::new(RwLock::new(Tensor::zeros(
head_dim as i64,
max_seq_len as i64,
TensorDType::Float32,
))));
cache_v.push(Arc::new(RwLock::new(Tensor::zeros(
head_dim as i64,
max_seq_len as i64,
TensorDType::Float32,
))));
}
AttentionCache { cache_k, cache_v }
}
}
pub struct RMSNorm {
eps: f64,
weight: Tensor,
}
pub struct Attention {
wq: Tensor,
wk: Tensor,
wv: Tensor,
wo: Tensor,
n_local_heads: usize,
head_dim: usize,
}
pub struct FeedForward {
w1: Tensor,
w2: Tensor,
w3: Tensor,
}
impl Transformer {
pub fn from_unpickled<P: AsRef<Path>>(
unpickled: &unpickler::Value,
emb: Embedding,
dim: usize,
n_layers: usize,
n_heads: usize,
max_seq_len: usize,
eps: f64,
n_local_heads: usize,
head_dim: usize,
data_dir: P,
) -> Result<Transformer, UnpicklingError> {
let data_dir: &Path = data_dir.as_ref();
let progress_bar = ProgressBar::new(n_layers as u64);
let layers: Vec<TransformerBlock> = (0..n_layers)
.into_par_iter()
.map(|layer_id| {
let result = TransformerBlock::from_unpickled(
unpickled,
layer_id,
eps,
n_local_heads,
head_dim,
data_dir,
);
progress_bar.inc(1);
result
})
.collect::<Result<Vec<TransformerBlock>, UnpicklingError>>()?;
std::mem::drop(progress_bar);
let norm = RMSNorm::from_unpickled(unpickled, format!("norm.weight"), eps, data_dir)?;
let output =
Tensor::from_unpickled(unpickled, format!("output.weight"), data_dir)?.to_f32();
Ok(Transformer {
freqs_cis: compute_freqs_cis(dim / n_heads, max_seq_len * 2, 10000.0),
emb,
dim,
n_layers,
n_heads,
n_local_heads,
max_seq_len,
head_dim,
norm,
output,
layers,
})
}
pub fn make_caches(&self) -> TransformerCaches {
let mut result = vec![];
for _ in 0..self.n_layers {
result.push(AttentionCache::new(
self.max_seq_len,
self.n_local_heads,
self.head_dim,
));
}
TransformerCaches {
layer_caches: result,
}
}
pub fn forward(
&self,
tokens: &[TokenId],
start_pos: usize,
caches: &mut TransformerCaches,
) -> Tensor {
assert!(caches.layer_caches.len() == self.n_layers);
let mask: Option<Tensor> = if tokens.len() > 1 {
Some(Tensor::full_triu(
tokens.len() as i64,
tokens.len() as i64,
start_pos as i64 + 1,
TensorDType::Float32,
std::f32::NEG_INFINITY,
))
} else {
None
};
let mut embs: Vec<&Tensor> = Vec::with_capacity(tokens.len());
for token in tokens.iter() {
let emb = self.emb.get_embedding(*token as usize);
embs.push(emb);
}
let mut emb_tensor: Tensor = Tensor::concat(&embs);
std::mem::drop(embs);
for (idx, layer) in self.layers.iter().enumerate() {
emb_tensor = layer.forward(
&emb_tensor,
start_pos,
&self.freqs_cis,
&mask,
&mut caches.layer_caches[idx],
);
}
let out = self.norm.forward(&emb_tensor);
let out = out.row(out.rows() - 1);
let prediction = self.output.matrix_mul_transposed(&out);
return prediction;
}
}
impl TransformerBlock {
pub fn from_unpickled<P: AsRef<Path>>(
unpickled: &unpickler::Value,
layer_id: usize,
eps: f64,
n_local_heads: usize,
head_dim: usize,
data_dir: P,
) -> Result<Self, UnpicklingError> {
let data_dir: &Path = data_dir.as_ref();
let ff = FeedForward::from_unpickled(unpickled, layer_id, data_dir)?;
let attn =
Attention::from_unpickled(unpickled, layer_id, n_local_heads, head_dim, data_dir)?;
let ffn_norm = RMSNorm::from_unpickled(
unpickled,
format!("layers.{}.ffn_norm.weight", layer_id),
eps,
data_dir,
)?;
let attn_norm = RMSNorm::from_unpickled(
unpickled,
format!("layers.{}.attention_norm.weight", layer_id),
eps,
data_dir,
)?;
Ok(Self {
feed_forward: ff,
attn,
ffn_norm,
attention_norm: attn_norm,
})
}
pub fn forward(
&self,
x: &Tensor,
start_pos: usize,
freqs_cis: &FreqsCis,
mask: &Option<Tensor>,
attention_cache: &mut AttentionCache,
) -> Tensor {
let attnorm_out = self.attention_norm.forward(x);
let att_out = self
.attn
.forward(&attnorm_out, start_pos, freqs_cis, mask, attention_cache);
let h = x.add(&att_out);
let att_out = self.ffn_norm.forward(&h);
let att_out = self.feed_forward.forward(&att_out.transpose()).transpose();
let att_out = h.add(&att_out);
return att_out;
}
}
impl RMSNorm {
pub fn from_unpickled<P: AsRef<Path>>(
unpickled: &unpickler::Value,
name: String,
eps: f64,
data_dir: P,
) -> Result<RMSNorm, UnpicklingError> {
let data_dir: &Path = data_dir.as_ref();
let weights = Tensor::from_unpickled(unpickled, &name, data_dir)?.to_f32();
Ok(Self {
eps,
weight: weights,
})
}
fn forward(&self, x: &Tensor) -> Tensor {
let inner = x.pow(2.0).mean_cols().add_scalar(self.eps as f32);
let out1 = x.scalar_multiply_broadcast(&inner.rsqrt());
return out1.hadamard_product_broadcast(&self.weight);
}
}
impl FeedForward {
pub fn from_unpickled<P: AsRef<Path>>(
unpickled: &unpickler::Value,
layer_id: usize,
data_dir: P,
) -> Result<FeedForward, UnpicklingError> {
let data_dir: &Path = data_dir.as_ref();
let w1 = Tensor::from_unpickled(
unpickled,
format!("layers.{}.feed_forward.w1.weight", layer_id),
data_dir,
)?
.to_f32();
let w2 = Tensor::from_unpickled(
unpickled,
format!("layers.{}.feed_forward.w2.weight", layer_id),
data_dir,
)?
.to_f32();
let w3 = Tensor::from_unpickled(
unpickled,
format!("layers.{}.feed_forward.w3.weight", layer_id),
data_dir,
)?
.to_f32();
Ok(Self { w1, w2, w3 })
}
pub fn forward(&self, x: &Tensor) -> Tensor {
let x = x.transpose();
let (w1_out, w3_out) = rayon::join(
|| self.w1.matrix_mul_transposed(&x),
|| self.w3.matrix_mul_transposed(&x),
);
let w1_out = w1_out.silu();
let w1w3_out = w1_out.hadamard_product(&w3_out).transpose();
let out = self.w2.matrix_mul_transposed(&w1w3_out);
return out;
}
}
impl Attention {
pub fn from_unpickled<P: AsRef<Path>>(
unpickled: &unpickler::Value,
layer_id: usize,
n_local_heads: usize,
head_dim: usize,
data_dir: P,
) -> Result<Attention, UnpicklingError> {
let data_dir: &Path = data_dir.as_ref();
let wq = Tensor::from_unpickled(
unpickled,
format!("layers.{}.attention.wq.weight", layer_id),
data_dir,
)?
.to_f32();
let wk = Tensor::from_unpickled(
unpickled,
format!("layers.{}.attention.wk.weight", layer_id),
data_dir,
)?
.to_f32();
let wv = Tensor::from_unpickled(
unpickled,
format!("layers.{}.attention.wv.weight", layer_id),
data_dir,
)?
.to_f32();
let wo = Tensor::from_unpickled(
unpickled,
format!("layers.{}.attention.wo.weight", layer_id),
data_dir,
)?
.to_f32();
Ok(Self {
wq,
wk,
wv,
wo,
n_local_heads,
head_dim,
})
}
fn forward(
&self,
x: &Tensor,
start_pos: usize,
freqs_cis: &FreqsCis,
mask: &Option<Tensor>,
attention_cache: &mut AttentionCache,
) -> Tensor {
let seq_len = x.rows();
let xq_out = x.matrix_mul_transposed(&self.wq);
let xk_out = x.matrix_mul_transposed(&self.wk);
let xv_out = x.matrix_mul_transposed(&self.wv);
let mut xq_views: Vec<Tensor> = Vec::with_capacity(seq_len as usize);
let mut xk_views: Vec<Tensor> = Vec::with_capacity(seq_len as usize);
let mut xv_views: Vec<Tensor> = Vec::with_capacity(seq_len as usize);
for idx in 0..seq_len {
let xq_row = xq_out
.row(idx)
.view(self.n_local_heads as i64, self.head_dim as i64);
let xk_row = xk_out
.row(idx)
.view(self.n_local_heads as i64, self.head_dim as i64);
let xv_row = xv_out
.row(idx)
.view(self.n_local_heads as i64, self.head_dim as i64);
let (xq_row, xk_row) =
apply_rotary_emb(&xq_row, &xk_row, freqs_cis, idx as usize, start_pos);
xq_views.push(xq_row);
xk_views.push(xk_row);
xv_views.push(xv_row);
}
let output: Vec<Tensor> = (0..self.n_local_heads)
.into_par_iter()
.map(|idx| {
let mut concat_vec: Vec<Tensor> = vec![];
for idx2 in 0..seq_len {
concat_vec.push(xq_views[idx2 as usize].row(idx as i64));
}
let concat_vec2: Vec<&Tensor> = concat_vec.iter().collect();
let xq_row = Tensor::concat(&concat_vec2);
concat_vec.truncate(0);
for idx2 in 0..seq_len {
concat_vec.push(xk_views[idx2 as usize].row(idx as i64));
}
let concat_vec2: Vec<&Tensor> = concat_vec.iter().collect();
let xk_row = Tensor::concat(&concat_vec2).transpose();
concat_vec.truncate(0);
for idx2 in 0..seq_len {
concat_vec.push(xv_views[idx2 as usize].row(idx as i64));
}
let concat_vec2: Vec<&Tensor> = concat_vec.iter().collect();
let xv_row = Tensor::concat(&concat_vec2);
let mut cache_k = attention_cache.cache_k[idx as usize].write().unwrap();
let mut cache_v = attention_cache.cache_v[idx as usize].write().unwrap();
/*
let m = xq_row
.matrix_mul(&xk_row)
.scalar_multiply_f32(1.0 / (self.head_dim as f32).sqrt());
//println!("mask size: {} {}", mask.rows(), mask.cols());
//println!("m size: {} {}", m.rows(), m.cols());
let m2 = m.add(mask).to_f32().softmax().matrix_mul(&xv_row);
m2
println!("xk_row size: {} {}", xk_row.rows(), xk_row.cols());
println!("xv_row size: {} {}", xv_row.rows(), xv_row.cols());
println!("cache_k size: {} {}", cache_k.rows(), cache_k.cols());
panic!("stop");
*/
for pos in start_pos..start_pos + seq_len as usize {
for dim in 0..self.head_dim {
let k = xk_row.get_f32(dim as i64, (pos - start_pos) as i64);
cache_k.set_f32(dim as i64, pos as i64, k);
let v = xv_row.get_f32((pos - start_pos) as i64, dim as i64);
cache_v.set_f32(dim as i64, pos as i64, v);
}
}
let keys = cache_k.clip_cols((start_pos + seq_len as usize) as usize);
let values = cache_v.clip_cols((start_pos + seq_len as usize) as usize);
let m = xq_row
.matrix_mul(&keys)
.scalar_multiply_f32(1.0 / (self.head_dim as f32).sqrt());
let m2 = match mask {
Some(ref mask) => m
.add(mask)
.to_f32()
.softmax()
.matrix_mul_transposed(&values),
None => m.softmax().matrix_mul_transposed(&values),
};
m2
})
.collect();
// convert from 32 matrices of size 8x128 to 8 matrices of size 32x128
// or rather 4096x1
let output2: Vec<Tensor> = (0..seq_len)
.into_par_iter()
.map(|idx| {
let mut concat_vec: Vec<Tensor> = vec![];
for idx2 in 0..self.n_local_heads {
concat_vec.push(output[idx2 as usize].row(idx as i64));
}
let concat_vec2: Vec<&Tensor> = concat_vec.iter().collect();
let xq_row = Tensor::concat(&concat_vec2).view(1, 4096);
let xq_row = xq_row.matrix_mul_transposed(&self.wo);
xq_row
})
.collect();
let output3: Vec<&Tensor> = output2.iter().collect();
let output2: Tensor = Tensor::concat(&output3);
return output2;
}
}
fn apply_rotary_emb(
xq: &Tensor,
xk: &Tensor,
freqs_cis: &FreqsCis,
seq_idx: usize,
start_pos: usize,
) -> (Tensor, Tensor) {
assert!(xq.cols() % 2 == 0);
assert!(xk.cols() % 2 == 0);
let mut xq_out: Tensor = xq.clone();
let mut xk_out: Tensor = xk.clone();
for row in 0..xq.rows() {
for col in 0..xq.cols() / 2 {
let f_real = freqs_cis[seq_idx + start_pos][col as usize].re as f32;
let f_imag = freqs_cis[seq_idx + start_pos][col as usize].im as f32;
let xq_real = xq.get_f32(row, col * 2);
let xq_imag = xq.get_f32(row, col * 2 + 1);
let xk_real = xk.get_f32(row, col * 2);
let xk_imag = xk.get_f32(row, col * 2 + 1);
// multiply with freqs_cis
let xq_realpart = xq_real * f_real - xq_imag * f_imag;
let xq_imagpart = xq_real * f_imag + xq_imag * f_real;
let xk_realpart = xk_real * f_real - xk_imag * f_imag;
let xk_imagpart = xk_real * f_imag + xk_imag * f_real;
xq_out.set_f32(row, col * 2, xq_realpart);
xq_out.set_f32(row, col * 2 + 1, xq_imagpart);
xk_out.set_f32(row, col * 2, xk_realpart);
xk_out.set_f32(row, col * 2 + 1, xk_imagpart);
}
}
return (xq_out, xk_out);
}
fn compute_freqs_cis(dim: usize, end: usize, theta: f64) -> FreqsCis {
let mut freqs = Vec::new();
for idx in 0..(dim / 2) {
let freq = 1.0 / (theta.powf(idx as f64 * 2.0 / dim as f64));
freqs.push(freq);
}
let mut result: Vec<Vec<f64>> = Vec::new();
for x in 0..end {
let mut row = Vec::new();
for y in 0..freqs.len() {
let freq = freqs[y] * (x as f64);
row.push(freq);
}
result.push(row);
}
let mut resultc: Vec<Vec<Complex<f64>>> = Vec::new();
for row in result.into_iter() {
let mut rowc = Vec::new();
for freq in row {
let cis = Complex::from_polar(1.0, freq);
rowc.push(cis);
}
resultc.push(rowc);
}
resultc
}

626
src/unpickler.rs
Unescape Escape View File

@ -0,0 +1,626 @@
use std::collections::BTreeMap;
use std::path::PathBuf;
pub struct Unpickler {}
use crate::tensor::{TensorBuilder, TensorDType, TensorError};
use thiserror::Error;
#[derive(Error, Debug)]
pub enum UnpicklingError {
#[error("Unpickling error: {0}")]
UnpicklingError(String),
#[error("UTF-8 decoding error")]
Utf8Error(#[from] std::str::Utf8Error),
#[error("Missing field")]
MissingField(String),
#[error("Tensor conversion operation failed")]
TensorError(#[from] TensorError),
#[error("Data has incorrect format to be converted to a tensor")]
InvalidTensorData,
}
#[derive(Clone, Debug, Eq, Ord, PartialEq, PartialOrd)]
pub enum Value {
Mark(usize),
String(String),
Global(String, String), // module name, attribute name
Integer64(i64),
Tuple(Vec<Value>),
PersistentId(Box<Value>),
Bool(bool),
Reduce(Box<Value>, Box<Value>),
Dict(BTreeMap<Value, Value>),
}
impl Value {
// Gets a value from a dictionary, assuming Value is a dictionary.
//
// Returns None if the key is not found, or the value is not a dictionary.
pub fn get(&self, key: &Value) -> Option<&Value> {
match self {
Value::Dict(d) => d.get(key),
_ => None,
}
}
// Same as get() but uses a string as key.
pub fn get_str_key<S: AsRef<str>>(&self, key: S) -> Option<&Value> {
self.get(&Value::String(key.as_ref().to_string()))
}
pub fn get_global(&self) -> Option<(&str, &str)> {
match self {
Value::Global(module_name, attribute_name) => Some((module_name, attribute_name)),
_ => None,
}
}
pub fn get_str(&self) -> Option<&str> {
match self {
Value::String(s) => Some(s),
_ => None,
}
}
pub fn get_int64(&self) -> Option<i64> {
match self {
Value::Integer64(i) => Some(*i),
_ => None,
}
}
pub fn get_persistent_id(&self) -> Option<&Value> {
match self {
Value::PersistentId(v) => Some(&v),
_ => None,
}
}
pub fn get_tuple(&self) -> Option<&[Value]> {
match self {
Value::Tuple(v) => Some(&v),
_ => None,
}
}
// Assume that the value represents a tensor in PyTorch and return instructions how to actually
// load the values.
pub fn to_tensor_builder(&self) -> Option<TensorBuilder> {
match self {
Value::Reduce(call, args) => match **call {
Value::Global(ref module_name, ref attribute_name) => {
if module_name == "torch._utils" && attribute_name == "_rebuild_tensor_v2" {
match **args {
Value::Tuple(ref args) => self.to_tensor_builder2(&args),
_ => None,
}
} else {
None
}
}
_ => None,
},
_ => None,
}
}
fn to_tensor_builder2(&self, args: &[Value]) -> Option<TensorBuilder> {
if args.len() == 6 {
Self::to_tensor_builder2_6items(args)
} else if args.len() == 4 {
Self::to_tensor_builder2_4items(args)
} else {
None
}
}
fn to_tensor_builder2_4items(args: &[Value]) -> Option<TensorBuilder> {
let storagev: &Value = args[0].get_persistent_id()?;
let storage_args: &[Value] = storagev.get_tuple()?;
let storage_mark: &str = storage_args[0].get_str()?;
if storage_mark != "storage" {
return None;
}
let (storage_module, storage_type) = storage_args[1].get_global()?;
if storage_module != "torch" {
return None;
}
let dtype: TensorDType = match storage_type {
"HalfStorage" => TensorDType::Float16,
_ => return None,
};
let storage_filename: &str = storage_args[2].get_str()?;
let nitems: i64 = storage_args[4].get_int64()?;
let offset: i64 = args[1].get_int64()?;
if offset != 0 {
return None;
}
let rows: i64 = 1;
let cols: i64 = nitems;
let row_stride: i64 = cols;
if row_stride != cols {
return None;
}
return Some(TensorBuilder {
src_path: PathBuf::from(storage_filename),
dtype,
stride: row_stride,
rows,
cols,
nitems,
});
}
fn to_tensor_builder2_6items(args: &[Value]) -> Option<TensorBuilder> {
let storagev: &Value = args[0].get_persistent_id()?;
let storage_args: &[Value] = storagev.get_tuple()?;
let storage_mark: &str = storage_args[0].get_str()?;
if storage_mark != "storage" {
return None;
}
let (storage_module, storage_type) = storage_args[1].get_global()?;
if storage_module != "torch" {
return None;
}
let dtype: TensorDType = match storage_type {
"HalfStorage" => TensorDType::Float16,
_ => return None,
};
let storage_filename: &str = storage_args[2].get_str()?;
let nitems: i64 = storage_args[4].get_int64()?;
let offset: i64 = args[1].get_int64()?;
if offset != 0 {
return None;
}
let shape: &[Value] = args[2].get_tuple()?;
let stride: &[Value] = args[3].get_tuple()?;
if shape.len() != 2 {
return None;
}
if stride.len() != 2 {
return None;
}
let rows: i64 = shape[0].get_int64()?;
let cols: i64 = shape[1].get_int64()?;
let row_stride: i64 = stride[0].get_int64()?;
let col_stride: i64 = stride[1].get_int64()?;
if col_stride != 1 {
return None;
}
if row_stride != cols {
return None;
}
return Some(TensorBuilder {
src_path: PathBuf::from(storage_filename),
dtype,
stride: row_stride,
rows,
cols,
nitems,
});
/* Args should look like this (took random example from debug print) :
0 PERSISTENT_ID
TUPLE
STRING "storage"
GLOBAL "torch" "HalfStorage"
STRING "0" (filename)
STRING "cpu"
INTEGER 131072000 (number of items)
1 INTEGER 0
2 TUPLE
INTEGER 32000
INTEGER 4096
3 TUPLE
INTEGER 4096
INTEGER 1
4 BOOL false (this is about gradient)
5 REDUCE (no idea why this is here)
GLOBAL "collections" "OrderedDict"
TUPLE
Sometimes arguments 2 and 3 are missing.
*/
}
// Print a nice representation of the value to stdout. Used for good old printf debugging.
pub fn debug_print(&self) {
self.debug_print_go(0);
}
fn debug_print_go(&self, indent: usize) {
if indent > 0 {
print!("{:indent$}", "", indent = indent);
}
match self {
Value::Mark(_) => {
println!("MARK");
}
Value::String(s) => {
println!("STRING {:?}", s);
}
Value::Global(module_name, attribute_name) => {
println!("GLOBAL {:?} {:?}", module_name, attribute_name);
}
Value::Integer64(i) => {
println!("INTEGER {:?}", i);
}
Value::Tuple(v) => {
println!("TUPLE");
for i in v {
i.debug_print_go(indent + 2);
}
}
Value::PersistentId(v) => {
println!("PERSISTENT_ID");
v.debug_print_go(indent + 2);
}
Value::Bool(b) => {
println!("BOOL {:?}", b);
}
Value::Reduce(v1, v2) => {
println!("REDUCE");
v1.debug_print_go(indent + 2);
v2.debug_print_go(indent + 2);
}
Value::Dict(d) => {
println!("DICT");
for (k, v) in d {
k.debug_print_go(indent + 2);
v.debug_print_go(indent + 2);
}
}
}
}
}
pub fn unpickle(bytes: &[u8]) -> Result<Value, UnpicklingError> {
// The LLaMA file is in pickle 2 format, check that header is there
if bytes.len() < 2 {
return Err(UnpicklingError::UnpicklingError(
"Data is too short to be a pickle".to_string(),
));
}
if bytes[0] != 128 || bytes[1] != 2 {
return Err(UnpicklingError::UnpicklingError(
"No magic header using Pickle 2 protocol".to_string(),
));
}
let mut memo: BTreeMap<u32, Value> = BTreeMap::new();
let mut stack: Vec<Value> = vec![];
// Decode frames
let mut bytes: &[u8] = &bytes[2..];
while !bytes.is_empty() {
let frame_opcode = bytes[0];
if frame_opcode == 125 {
// empty dict
stack.push(Value::Dict(BTreeMap::new()));
bytes = &bytes[1..];
continue;
}
if frame_opcode == 113 {
// binput
if bytes.len() < 2 {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling BINPUT".to_string(),
));
}
if stack.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Stack is empty while handling BINPUT".to_string(),
));
}
let key = bytes[1];
memo.insert(key as u32, stack.last().unwrap().clone());
bytes = &bytes[2..];
continue;
}
if frame_opcode == 40 {
// mark
stack.push(Value::Mark(stack.len()));
bytes = &bytes[1..];
continue;
}
if frame_opcode == 88 {
// binunicode
if bytes.len() < 5 {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling BINUNICODE".to_string(),
));
}
let len = u32::from_le_bytes([bytes[1], bytes[2], bytes[3], bytes[4]]);
if bytes.len() < 5 + len as usize {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling BINUNICODE".to_string(),
));
}
let string = std::str::from_utf8(&bytes[5..5 + len as usize])?;
stack.push(Value::String(string.to_string()));
bytes = &bytes[5 + len as usize..];
continue;
}
if frame_opcode == 99 {
// global
// followed by newline terminated module name and attribute name
bytes = &bytes[1..];
let mut module_name = String::new();
while !bytes.is_empty() && bytes[0] != 10 {
module_name.push(bytes[0] as char);
bytes = &bytes[1..];
if bytes.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling GLOBAL".to_string(),
));
}
}
bytes = &bytes[1..];
let mut attribute_name = String::new();
while !bytes.is_empty() && bytes[0] != 10 {
attribute_name.push(bytes[0] as char);
bytes = &bytes[1..];
if bytes.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling GLOBAL".to_string(),
));
}
}
bytes = &bytes[1..];
stack.push(Value::Global(module_name, attribute_name));
continue;
}
if frame_opcode == 74 {
// binint
if bytes.len() < 5 {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling BININT".to_string(),
));
}
let value = i32::from_le_bytes([bytes[1], bytes[2], bytes[3], bytes[4]]);
stack.push(Value::Integer64(value as i64));
bytes = &bytes[5..];
continue;
}
if frame_opcode == 116 {
// tuple
let mut tuple = vec![];
if stack.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Stack is empty while handling TUPLE".to_string(),
));
}
let mut ok = false;
while !stack.is_empty() {
let top = stack.pop().unwrap();
if let Value::Mark(_mark) = top {
tuple.reverse();
stack.push(Value::Tuple(tuple));
ok = true;
break;
}
tuple.push(top);
}
if !ok {
return Err(UnpicklingError::UnpicklingError(
"No mark while handling TUPLE".to_string(),
));
}
bytes = &bytes[1..];
continue;
}
if frame_opcode == 81 {
// binpersid
if stack.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Stack is empty while handling BINPERSID".to_string(),
));
}
let top = stack.pop().unwrap();
stack.push(Value::PersistentId(Box::new(top)));
bytes = &bytes[1..];
continue;
}
if frame_opcode == 75 {
// binint1
if bytes.len() < 2 {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling BININT1".to_string(),
));
}
let value = bytes[1];
stack.push(Value::Integer64(value as i64));
bytes = &bytes[2..];
continue;
}
if frame_opcode == 77 {
// binint2
if bytes.len() < 3 {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling BININT2".to_string(),
));
}
let value = i16::from_le_bytes([bytes[1], bytes[2]]);
stack.push(Value::Integer64(value as i64));
bytes = &bytes[3..];
continue;
}
if frame_opcode == 134 {
// tuple2
let mut tuple = vec![];
if stack.len() < 2 {
return Err(UnpicklingError::UnpicklingError(
"Stack does not have enough items while handling TUPLE2".to_string(),
));
}
tuple.push(stack.pop().unwrap());
tuple.push(stack.pop().unwrap());
tuple.reverse();
stack.push(Value::Tuple(tuple));
bytes = &bytes[1..];
continue;
}
if frame_opcode == 137 {
// newfalse
stack.push(Value::Bool(false));
bytes = &bytes[1..];
continue;
}
if frame_opcode == 41 {
// empty tuple
stack.push(Value::Tuple(vec![]));
bytes = &bytes[1..];
continue;
}
if frame_opcode == 82 {
// reduce
if stack.len() < 2 {
return Err(UnpicklingError::UnpicklingError(
"Stack does not have enough items while handling REDUCE".to_string(),
));
}
let arg_tuple = stack.pop().unwrap();
let callable = stack.pop().unwrap();
stack.push(Value::Reduce(Box::new(callable), Box::new(arg_tuple)));
bytes = &bytes[1..];
continue;
}
if frame_opcode == 104 {
// binget
if bytes.len() < 2 {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling BINGET".to_string(),
));
}
let idx = bytes[1];
match memo.get(&(idx as u32)) {
None => {
return Err(UnpicklingError::UnpicklingError(
"BINGET index out of range".to_string(),
));
}
Some(memo_value) => {
stack.push(memo_value.clone());
}
}
bytes = &bytes[2..];
continue;
}
if frame_opcode == 133 {
// tuple1
let mut tuple = vec![];
if stack.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Stack is empty while handling TUPLE1".to_string(),
));
}
tuple.push(stack.pop().unwrap());
bytes = &bytes[1..];
continue;
}
if frame_opcode == 114 {
// long binput
if bytes.len() < 5 {
return Err(UnpicklingError::UnpicklingError(
"Unexpected end of data while handling LONG_BINPUT".to_string(),
));
}
let key = u32::from_le_bytes([bytes[1], bytes[2], bytes[3], bytes[4]]);
if stack.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Stack is empty while handling LONG_BINPUT".to_string(),
));
}
memo.insert(key as u32, stack.last().unwrap().clone());
bytes = &bytes[5..];
continue;
}
if frame_opcode == 117 {
// setitems
if stack.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Stack is empty while handling SETITEMS".to_string(),
));
}
let mut ok = false;
let mut keyvalues: BTreeMap<Value, Value> = BTreeMap::new();
while !stack.is_empty() {
let value = stack.pop().unwrap();
if let Value::Mark(_mark) = value {
ok = true;
break;
}
if stack.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Stack is empty while handling SETITEMS".to_string(),
));
}
let key = stack.pop().unwrap();
if let Value::Mark(_mark) = key {
return Err(UnpicklingError::UnpicklingError(
"Unexpected mark while handling SETITEMS".to_string(),
));
}
keyvalues.insert(key, value);
}
if !ok {
return Err(UnpicklingError::UnpicklingError(
"No mark while handling SETITEMS".to_string(),
));
}
if stack.is_empty() {
return Err(UnpicklingError::UnpicklingError(
"Stack is empty while handling SETITEMS".to_string(),
));
}
let mut dict = stack.pop().unwrap();
match dict {
Value::Dict(ref mut dict) => {
for (key, value) in keyvalues {
dict.insert(key, value);
}
}
_ => {
return Err(UnpicklingError::UnpicklingError(
"SETITEMS on non-dict".to_string(),
));
}
}
stack.push(dict);
bytes = &bytes[1..];
continue;
}
if frame_opcode == 46 {
// stop
// bytes = &bytes[1..];
break;
}
return Err(UnpicklingError::UnpicklingError(format!(
"Unknown opcode: {}",
frame_opcode
)));
}
// Stack should have just one item, our final value
if stack.len() != 1 {
return Err(UnpicklingError::UnpicklingError(
"Stack does not have exactly one item after unpickling".to_string(),
));
}
Ok(stack.pop().unwrap())
}
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