repo stringclasses 20
values | path stringlengths 6 94 | lang stringclasses 5
values | n_chars int64 81 200k | sha256 stringlengths 64 64 | content stringlengths 81 200k |
|---|---|---|---|---|---|
eren23/synapse | synapse/synapse-esp32/build.rs | rs | 278 | d8340add2983eab773e12a0ede217afe65aca9fb4ed257e925c55b923396eea3 | fn main() {
// Only emit ESP-IDF sysenv when cross-compiling for espidf targets.
// Host builds (cargo test -p synapse-esp32) skip this entirely.
if std::env::var("CARGO_CFG_TARGET_OS").as_deref() == Ok("espidf") {
embuild::espidf::sysenv::output();
}
}
|
eren23/synapse | synapse/synapse-esp32/examples/lewm_rollout_bench.rs | rs | 2,610 | 0f1c2d3103a49da78393e4c2cbae57dfd8d6dd349bc5a107d0a3dedd65029e5a | //! Benchmark: sequential vs fused rollout latency.
use synapse_esp32::model::Esp32LeWM;
fn det(len: usize, seed: u32) -> Vec<f32> {
(0..len)
.map(|i| {
let m = seed.wrapping_mul(1_664_525).wrapping_add((i as u32).wrapping_mul(1_013_904_223));
let centered = (m % 2_001).wrapping_sub... |
eren23/synapse | synapse/synapse-esp32/examples/lewm_probe.rs | rs | 7,950 | 35af2e49b5d50efb9c1b31cf835a92e0e4c7a5644d6775d2c3c9b711e8052782 | use std::path::PathBuf;
use synapse_inference::ops::pure_rust_ops::{gelu, layernorm, matmul_t};
use synapse_inference::quantization::QuantizedQ4LeWM;
fn main() {
let mut model_path: Option<PathBuf> = None;
let args: Vec<String> = std::env::args().collect();
let mut i = 1;
while i < args.len() {
... |
eren23/synapse | synapse/synapse-esp32/examples/lewm_golden.rs | rs | 2,234 | 4b5e45df030c1ab8bb6fe2515b29de27b3d194485ee90caa57bf8bc90a87776a | use std::path::PathBuf;
use synapse_esp32::model::Esp32LeWM;
fn main() {
let mut model_path: Option<PathBuf> = None;
let mut steps: usize = 5;
let args: Vec<String> = std::env::args().collect();
let mut i = 1;
while i < args.len() {
match args[i].as_str() {
"--model" | "-m" =>... |
eren23/synapse | synapse/synapse-esp32/examples/lewm_encode_probe.rs | rs | 4,354 | 2c389456a0bfc5ad3246a4d8ab84af538c9fc2a939dcf21a56fb046d8daa0cc5 | use std::path::PathBuf;
use synapse_esp32::model::Esp32LeWM;
use synapse_inference::ops::patch_embed::patch_embed;
use synapse_inference::ops::pure_rust_ops::layernorm;
use synapse_inference::quantization::FullyQuantizedLeWM;
fn main() {
let mut model_path: Option<PathBuf> = None;
let args: Vec<String> = std... |
eren23/synapse | synapse/synapse-esp32/src/lib.rs | rs | 539 | 1d99d73f5951e8e4c2fa708ca2346ac09712065ab53e86caf57e765cd47a7091 | //! Synapse ESP32-P4: multi-model inference on a $10 RISC-V microcontroller.
//!
//! Supported models:
//! - LeWM (world model): encode, predict, rollout
//! - Mamba Q4 (language model): text generation
//! - RWKV-7 Q4 (language model): text generation
//!
//! Architecture:
//! Phone camera / text -> WiFi HTTP ... |
eren23/synapse | synapse/synapse-esp32/src/server.rs | rs | 11,352 | 8ab471455ccdf45d4dc59017105781f93d827f5ed01e323872028852d5e5a71b | //! HTTP server for inference over WiFi.
//!
//! Endpoints:
//! POST /encode -- image -> latent (LeWM only)
//! POST /predict -- latent + action -> next latent (LeWM only)
//! POST /rollout -- latent + actions -> trajectory (LeWM only)
//! POST /llm/generate -- prompt tokens -> generated tokens (Mamba/R... |
eren23/synapse | synapse/synapse-esp32/src/main.rs | rs | 7,658 | 04f0632cdf7ab85519862b6c6d5626b09a309528b7b43ee36b040237a4776bbf | //! ESP32-P4 multi-model inference server.
//!
//! On real hardware (--features esp32):
//! Connects to WiFi, starts HTTP server, serves inference endpoints.
//!
//! On host (default, --features host-test):
//! Runs a quick smoke test of all model types and server handlers.
#[cfg(all(feature = "host-test", feature... |
eren23/synapse | synapse/synapse-esp32/src/model.rs | rs | 30,094 | 9f80fb4e38ec612923b5d9ea7e8c87c5d06f8b5504f0a048ef4788e516ed465c | //! Model loading and inference for ESP32.
//!
//! Supports multiple model types:
//! - LEWM world model (encode/predict/rollout) — f32 or Q4 quantized
//! - Mamba Q4 language model (text generation)
//! - RWKV-7 Q4 language model (text generation)
use std::time::Instant;
use synapse_inference::models::ssm::mamba::blo... |
eren23/synapse | synapse/crates/synapse-train/src/checkpoint.rs | rs | 5,829 | 9ee879152bb84cbcd45154f33aee04c3ddd2ecf29586662695b906198edd69ae | use std::collections::BTreeMap;
use std::io::{self, Cursor, Read, Write};
const MAGIC: &[u8; 4] = b"SYNP";
const VERSION: u32 = 1;
/// Model state dictionary: parameter name -> (shape, data).
pub type StateDict = BTreeMap<String, (Vec<usize>, Vec<f32>)>;
/// Serialize a state dict to a writer in a binary format.
///... |
eren23/synapse | synapse/crates/synapse-train/src/lib.rs | rs | 450 | 191bedb6a82d22c72757b28723a3b84ceaba3addb2d4d8c2394dd8ede9ac2a0a | pub mod callback;
pub mod checkpoint;
pub mod metrics;
pub mod progress;
pub mod trainer;
pub use callback::{CallbackAction, EarlyStopping, ModelCheckpoint, TrainerCallback};
pub use checkpoint::{load_checkpoint, load_from_bytes, save_checkpoint, save_to_bytes, StateDict};
pub use metrics::{Accuracy, ConfusionMatrix, ... |
eren23/synapse | synapse/crates/synapse-train/src/callback.rs | rs | 5,718 | a90880593b99e307723e2f2aa8eedf516b8dcacf54a98558e0a722611c55e0ec | use crate::trainer::{EpochResult, TrainHistory};
/// Action returned by callbacks to control the training loop.
pub enum CallbackAction {
Continue,
Stop,
}
/// Trait for hooks into the training loop.
pub trait TrainerCallback {
fn on_epoch_start(&mut self, _epoch: usize) {}
fn on_epoch_end(&mut self, ... |
eren23/synapse | synapse/crates/synapse-train/src/metrics.rs | rs | 6,906 | 425fb5254be83725f0770fa806eb96932417dfe10b3f418fa4aae5388494312d | /// Tracks a running mean of scalar values.
pub struct RunningMean {
sum: f64,
count: usize,
}
impl RunningMean {
pub fn new() -> Self {
RunningMean { sum: 0.0, count: 0 }
}
pub fn update(&mut self, value: f32) {
self.sum += value as f64;
self.count += 1;
}
pub fn ... |
eren23/synapse | synapse/crates/synapse-train/src/progress.rs | rs | 3,411 | 5a4b91a51ccd13435e8db1b82fdca8aca10af5a2c15bd4dc395c04e00eecea83 | use std::time::{Duration, Instant};
/// Tracks progress through epochs and batches, providing ETA estimates.
pub struct ProgressTracker {
total_epochs: usize,
current_epoch: usize,
total_batches: usize,
current_batch: usize,
start_time: Instant,
epoch_start: Instant,
}
impl ProgressTracker {
... |
eren23/synapse | synapse/crates/synapse-train/src/trainer.rs | rs | 5,051 | 50a26206f49fbabd1d735d5fddfa31adca1190833af87465205acdf7b4fa2eb4 | use std::time::Instant;
use synapse_autograd::Tensor;
use crate::callback::{CallbackAction, TrainerCallback};
use crate::metrics::RunningMean;
use crate::progress::ProgressTracker;
/// Configuration for the training loop.
pub struct TrainerConfig {
pub epochs: usize,
}
/// Result of a single training epoch.
pub... |
eren23/synapse | synapse/crates/synapse-data/src/text_dataset.rs | rs | 6,656 | 72c2e5fd7c7620ea8b11843b7f34280cf101bf07a4813430ef513d5cd027cde7 | use crate::collate::pad_sequences;
use crate::dataset::Dataset;
use crate::tokenizer::{WhitespaceTokenizer, PAD_ID};
use crate::Tensor;
/// A text classification dataset that loads tab-separated `label\ttext` lines.
///
/// Each sample is stored as `(token_ids, label)` and returned as
/// `[token_ids_tensor, label_ten... |
eren23/synapse | synapse/crates/synapse-data/src/collate.rs | rs | 5,132 | 153fdfe5f7a194fe2b6a80cf028962dde6807ef733e4140e4b6ff40999001da4 | use crate::Tensor;
/// Default collate function: given a batch of samples (each a `Vec<Tensor>`),
/// stack corresponding tensors along a new leading batch dimension.
///
/// For N samples each containing K tensors, produces K tensors each with
/// a new leading dimension of size N.
///
/// Example: 4 samples of [feat... |
eren23/synapse | synapse/crates/synapse-data/src/lib.rs | rs | 5,660 | 10706b57178ed6f5aa4f0960111969369563c657d631877786bfef75380e0258 | pub mod collate;
pub mod dataloader;
pub mod dataset;
pub mod sampler;
pub mod text_dataset;
pub mod tokenizer;
pub mod transform;
use std::fmt;
/// A simple N-dimensional tensor backed by contiguous f32 data in row-major order.
#[derive(Clone)]
pub struct Tensor {
data: Vec<f32>,
shape: Vec<usize>,
}
impl f... |
eren23/synapse | synapse/crates/synapse-data/src/dataloader.rs | rs | 13,867 | 862066b4523f9fc61074b0efd80fe701c19b4ed69016c7c31959523f5625200d | use std::sync::mpsc::{sync_channel, Receiver};
use std::sync::Arc;
use std::thread::{self, JoinHandle};
use crate::collate::default_collate;
use crate::dataset::Dataset;
use crate::sampler::{RandomSampler, Sampler, SequentialSampler};
use crate::Tensor;
/// A configurable data loader that batches dataset samples with... |
eren23/synapse | synapse/crates/synapse-data/src/sampler.rs | rs | 5,793 | 2343c247ae95734aaf2f52e3d4ee78303c50b054da3f03de9a72529c8b04c0fb | use rand::rngs::StdRng;
use rand::seq::SliceRandom;
use rand::{Rng, SeedableRng};
/// A sampler produces a sequence of dataset indices.
pub trait Sampler: Send {
/// Reset the sampler for a new epoch, returning an iterator over indices.
fn indices(&mut self) -> Vec<usize>;
fn len(&self) -> usize;
}
/// Yi... |
eren23/synapse | synapse/crates/synapse-data/src/transform.rs | rs | 4,863 | e6c4fc5d2f69652d42bd657a6a4786b5852173b0b19ed3b4258a1e3a4422550f | use crate::Tensor;
use rand::Rng;
/// A transform modifies tensor data.
pub trait Transform: Send + Sync {
fn apply(&self, tensor: &Tensor) -> Tensor;
}
/// Normalize: `(x - mean) / std` element-wise.
pub struct Normalize {
pub mean: f32,
pub std: f32,
}
impl Normalize {
pub fn new(mean: f32, std: f3... |
eren23/synapse | synapse/crates/synapse-data/src/tokenizer.rs | rs | 10,711 | b0fd225d387793da21b62164daf1a1cecbd2a0e442e8cfad498542a837cbfb7e | use std::collections::HashMap;
/// Special token IDs reserved at the start of every vocabulary.
pub const PAD_ID: usize = 0;
pub const UNK_ID: usize = 1;
pub const BOS_ID: usize = 2;
pub const EOS_ID: usize = 3;
const PAD_TOKEN: &str = "<PAD>";
const UNK_TOKEN: &str = "<UNK>";
const BOS_TOKEN: &str = "<BOS>";
const E... |
eren23/synapse | synapse/crates/synapse-data/src/dataset.rs | rs | 2,926 | 950bfc56071649f4c4908bbb11484fb41587a789dbea61e844a6e8b64b2e8731 | use crate::Tensor;
/// A dataset provides indexed access to samples.
/// Each sample is a `Vec<Tensor>` (e.g., [features, labels]).
pub trait Dataset: Send + Sync {
fn len(&self) -> usize;
fn get(&self, index: usize) -> Vec<Tensor>;
fn is_empty(&self) -> bool {
self.len() == 0
}
}
/// A datase... |
eren23/synapse | synapse/crates/synapse-code-tokenizer/tests/cross_validation.rs | rs | 3,730 | 6088795ba95615e756cfa033a387c5be37c8b2752f8bcdeeb8e970e2d2794b2f | //! Byte-for-byte validation against the Python FNV-1a reference
//! (scripts/ast_tokenizer_fnv.py). If Python-produced tokens match exactly,
//! the Rust port is drop-in equivalent.
use synapse_code_tokenizer::tokenize;
/// Each case: (rust_source, expected_tokens from Python ast_tokenizer_fnv.py)
const CASES: &[(&s... |
eren23/synapse | synapse/crates/synapse-code-tokenizer/src/lib.rs | rs | 30,968 | 3879bd33c71a8f7a9a01bdd7d4f5c4644192c3ad0bc214dde374efd79a8b9d2d | //! Python AST tokenizer for Code WM — matches `ast_tokenizer.ast_tokenize()`
//! from the training tap, implemented in Rust via rustpython-parser.
//!
//! This removes the Python runtime dependency from Code WM inference and
//! enables tokenization on any target Rust compiles to (WASM, ESP32, native).
//!
//! ## Voca... |
eren23/synapse | synapse/crates/synapse-inference/build.rs | rs | 364 | 32f8e9ffe0c2d45a78de2f242bb4ca538df950123401deb8719ddb471b0b9dbb | fn main() {
// Link Apple Accelerate framework for cblas_sgemm — only when targeting macOS.
// Uses TARGET env var (not cfg!) because build.rs runs on the host.
let target = std::env::var("TARGET").unwrap_or_default();
if target.contains("apple") && !target.contains("wasm") {
println!("cargo:rus... |
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