From c159b2e13021665c1bf9325f16ef27c7341dbd47 Mon Sep 17 00:00:00 2001 From: NeTT Date: Tue, 24 Sep 2024 15:32:09 +0530 Subject: [PATCH] fix wasm --- crates/core/Cargo.toml | 2 +- crates/core/src/cpu/backend.rs | 40 +++++++++--------- crates/core/src/ffi.rs | 16 +++++-- crates/core/src/util.rs | 6 +++ crates/core/src/wasm.rs | 31 ++++++++++---- examples/classification/binary_iris.ts | 6 ++- packages/core/src/backends/wasm/backend.ts | 34 ++++++++++++--- .../backends/wasm/lib/netsaur.generated.js | 19 ++++++--- .../src/backends/wasm/lib/netsaur_bg.wasm | Bin 1102034 -> 955054 bytes 9 files changed, 109 insertions(+), 45 deletions(-) diff --git a/crates/core/Cargo.toml b/crates/core/Cargo.toml index f28d4b8..c22ec09 100644 --- a/crates/core/Cargo.toml +++ b/crates/core/Cargo.toml @@ -16,4 +16,4 @@ safetensors = { workspace = true } [target.'cfg(target_arch = "wasm32")'.dependencies] wasm-bindgen = "0.2.92" getrandom = { version = "0.2", features = ["js"] } -js-sys = "0.3.69" +js-sys = "0.3.69" \ No newline at end of file diff --git a/crates/core/src/cpu/backend.rs b/crates/core/src/cpu/backend.rs index 25bd809..b74dc4b 100644 --- a/crates/core/src/cpu/backend.rs +++ b/crates/core/src/cpu/backend.rs @@ -1,5 +1,4 @@ use std::collections::HashMap; -use std::time::Instant; use ndarray::{ArrayD, ArrayViewD, IxDyn}; use safetensors::{serialize, SafeTensors}; @@ -9,7 +8,7 @@ use crate::{ BatchNormTensors, CPUCost, CPULayer, CPUOptimizer, CPUPostProcessor, CPUScheduler, Conv2DCPULayer, ConvTensors, ConvTranspose2DCPULayer, Dataset, DenseCPULayer, DenseTensors, Dropout1DCPULayer, Dropout2DCPULayer, FlattenCPULayer, GetTensor, Layer, Logger, - Pool2DCPULayer, PostProcessor, SoftmaxCPULayer, Tensor, Tensors, + Pool2DCPULayer, PostProcessor, SoftmaxCPULayer, Tensor, Tensors, Timer, }; pub struct Backend { @@ -23,10 +22,16 @@ pub struct Backend { pub optimizer: CPUOptimizer, pub scheduler: CPUScheduler, pub logger: Logger, + pub timer: Timer, } impl Backend { - pub fn new(config: BackendConfig, logger: Logger, mut tensors: Option>) -> Self { + pub fn new( + config: BackendConfig, + logger: Logger, + timer: Timer, + mut tensors: Option>, + ) -> Self { let mut layers = Vec::new(); let mut size = config.size.clone(); for layer in config.layers.iter() { @@ -99,6 +104,7 @@ impl Backend { optimizer, scheduler, size, + timer, } } @@ -147,7 +153,7 @@ impl Backend { let mut cost = 0f32; let mut time: u128; let mut total_time = 0u128; - let start = Instant::now(); + let start = (self.timer.now)(); let total_iter = epochs * datasets.len(); while epoch < epochs { let mut total = 0.0; @@ -160,11 +166,11 @@ impl Backend { let minibatch = outputs.dim()[0]; if !self.silent && ((i + 1) * minibatch) % batches == 0 { cost = total / (batches) as f32; - time = start.elapsed().as_millis() - total_time; + time = ((self.timer.now)() - start) - total_time; total_time += time; let current_iter = epoch * datasets.len() + i; let msg = format!( - "Epoch={}, Dataset={}, Cost={}, Time={}s, ETA={}s", + "Epoch={}, Dataset={}, Cost={}, Time={:.3}s, ETA={:.3}s", epoch, i * minibatch, cost, @@ -188,25 +194,20 @@ impl Backend { } else { disappointments += 1; if !self.silent { - println!( + (self.logger.log)(format!( "Patience counter: {} disappointing epochs out of {}.", disappointments, self.patience - ); + )); } } if disappointments >= self.patience { if !self.silent { - println!( + (self.logger.log)(format!( "No improvement for {} epochs. Stopping early at cost={}", disappointments, best_cost - ); + )); } - let net = Self::load( - &best_net, - Logger { - log: |x| println!("{}", x), - }, - ); + let net = Self::load(&best_net, self.logger.clone(), self.timer.clone()); self.layers = net.layers; break; } @@ -225,7 +226,8 @@ impl Backend { for layer in &mut self.layers { layer.reset(1); } - processor.process(self.forward_propagate(data, false, layers)) + let res = self.forward_propagate(data, false, layers); + processor.process(res) } pub fn save(&self) -> Vec { @@ -278,7 +280,7 @@ impl Backend { serialize(tensors, &Some(metadata)).unwrap() } - pub fn load(buffer: &[u8], logger: Logger) -> Self { + pub fn load(buffer: &[u8], logger: Logger, timer: Timer) -> Self { let tensors = SafeTensors::deserialize(buffer).unwrap(); let (_, metadata) = SafeTensors::read_metadata(buffer).unwrap(); let data = metadata.metadata().as_ref().unwrap(); @@ -310,6 +312,6 @@ impl Backend { }; } - Backend::new(config, logger, Some(layers)) + Backend::new(config, logger, timer, Some(layers)) } } diff --git a/crates/core/src/ffi.rs b/crates/core/src/ffi.rs index 93e2abb..26b02ee 100644 --- a/crates/core/src/ffi.rs +++ b/crates/core/src/ffi.rs @@ -1,8 +1,9 @@ use std::slice::{from_raw_parts, from_raw_parts_mut}; +use std::time::{SystemTime, UNIX_EPOCH}; use crate::{ - decode_array, decode_json, length, Backend, Dataset, Logger, PredictOptions, TrainOptions, - RESOURCES, + decode_array, decode_json, length, Backend, Dataset, Logger, PredictOptions, Timer, + TrainOptions, RESOURCES, }; type AllocBufferFn = extern "C" fn(usize) -> *mut u8; @@ -11,10 +12,17 @@ fn log(string: String) { println!("{}", string) } +fn now() -> u128 { + SystemTime::now() + .duration_since(UNIX_EPOCH) + .expect("Your system is behind the Unix Epoch") + .as_millis() +} + #[no_mangle] pub extern "C" fn ffi_backend_create(ptr: *const u8, len: usize, alloc: AllocBufferFn) -> usize { let config = decode_json(ptr, len); - let net_backend = Backend::new(config, Logger { log }, None); + let net_backend = Backend::new(config, Logger { log }, Timer { now }, None); let buf: Vec = net_backend .size .iter() @@ -98,7 +106,7 @@ pub extern "C" fn ffi_backend_load( alloc: AllocBufferFn, ) -> usize { let buffer = unsafe { from_raw_parts(file_ptr, file_len) }; - let net_backend = Backend::load(buffer, Logger { log }); + let net_backend = Backend::load(buffer, Logger { log }, Timer { now }); let buf: Vec = net_backend.size.iter().map(|x| *x as u8).collect(); let size_ptr = alloc(buf.len()); let output_shape = unsafe { from_raw_parts_mut(size_ptr, buf.len()) }; diff --git a/crates/core/src/util.rs b/crates/core/src/util.rs index edee1a0..31e6432 100644 --- a/crates/core/src/util.rs +++ b/crates/core/src/util.rs @@ -4,10 +4,16 @@ use ndarray::ArrayD; use safetensors::tensor::TensorView; use serde::Deserialize; +#[derive(Clone)] pub struct Logger { pub log: fn(string: String) -> (), } +#[derive(Clone)] +pub struct Timer { + pub now: fn() -> u128, +} + pub fn length(shape: Vec) -> usize { return shape.iter().fold(1, |i, x| i * x); } diff --git a/crates/core/src/wasm.rs b/crates/core/src/wasm.rs index d3709b4..4f2e335 100644 --- a/crates/core/src/wasm.rs +++ b/crates/core/src/wasm.rs @@ -1,26 +1,39 @@ use js_sys::{Array, Float32Array, Uint8Array}; use ndarray::ArrayD; - use wasm_bindgen::{prelude::wasm_bindgen, JsValue}; -use crate::{Backend, Dataset, Logger, PredictOptions, TrainOptions, RESOURCES}; +use crate::{Backend, Dataset, Logger, PredictOptions, Timer, TrainOptions, RESOURCES}; #[wasm_bindgen] extern "C" { #[wasm_bindgen(js_namespace = console)] fn log(s: &str); + #[wasm_bindgen(js_namespace = Date)] + fn now() -> f64; + } fn console_log(string: String) { log(string.as_str()) } +fn performance_now() -> u128 { + now() as u128 +} + #[wasm_bindgen] pub fn wasm_backend_create(config: String, shape: Array) -> usize { let config = serde_json::from_str(&config).unwrap(); let mut len = 0; let logger = Logger { log: console_log }; - let net_backend = Backend::new(config, logger, None); + let net_backend = Backend::new( + config, + logger, + Timer { + now: performance_now, + }, + None, + ); shape.set_length(net_backend.size.len() as u32); for (i, s) in net_backend.size.iter().enumerate() { shape.set(i as u32, JsValue::from(*s)) @@ -37,7 +50,6 @@ pub fn wasm_backend_create(config: String, shape: Array) -> usize { #[wasm_bindgen] pub fn wasm_backend_train(id: usize, buffers: Vec, options: String) { let options: TrainOptions = serde_json::from_str(&options).unwrap(); - let mut datasets = Vec::new(); for i in 0..options.datasets { let input = buffers[i * 2].to_vec(); @@ -47,7 +59,6 @@ pub fn wasm_backend_train(id: usize, buffers: Vec, options: String outputs: ArrayD::from_shape_vec(options.output_shape.clone(), output).unwrap(), }); } - RESOURCES.with(|cell| { let mut backend = cell.backend.borrow_mut(); backend[id].train(datasets, options.epochs, options.batches, options.rate) @@ -59,11 +70,12 @@ pub fn wasm_backend_predict(id: usize, buffer: Float32Array, options: String) -> let options: PredictOptions = serde_json::from_str(&options).unwrap(); let inputs = ArrayD::from_shape_vec(options.input_shape, buffer.to_vec()).unwrap(); - let res = ArrayD::zeros(options.output_shape); + let mut res = ArrayD::zeros(options.output_shape.clone()); RESOURCES.with(|cell| { let mut backend = cell.backend.borrow_mut(); - let _res = backend[id].predict(inputs, options.layers); + let _res = backend[id].predict(inputs, options.post_process, options.layers); + res.assign(&ArrayD::from_shape_vec(options.output_shape, _res.as_slice().unwrap().to_vec()).unwrap()); }); Float32Array::from(res.as_slice().unwrap()) } @@ -82,7 +94,10 @@ pub fn wasm_backend_save(id: usize) -> Uint8Array { pub fn wasm_backend_load(buffer: Uint8Array, shape: Array) -> usize { let mut len = 0; let logger = Logger { log: console_log }; - let net_backend = Backend::load(buffer.to_vec().as_slice(), logger); + let timer = Timer { + now: performance_now, + }; + let net_backend = Backend::load(buffer.to_vec().as_slice(), logger, timer); shape.set_length(net_backend.size.len() as u32); for (i, s) in net_backend.size.iter().enumerate() { shape.set(i as u32, JsValue::from(*s)) diff --git a/examples/classification/binary_iris.ts b/examples/classification/binary_iris.ts index 28d366f..1a51dc8 100644 --- a/examples/classification/binary_iris.ts +++ b/examples/classification/binary_iris.ts @@ -17,6 +17,7 @@ import { useSplit, } from "../../packages/utilities/mod.ts"; import { PostProcess } from "../../packages/core/src/core/api/postprocess.ts"; +import { AdamOptimizer, WASM } from "../../mod.ts"; // Define classes const classes = ["Setosa", "Versicolor"]; @@ -33,7 +34,7 @@ const y = data.map((fl) => classes.indexOf(fl[4])); const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y); // Setup the CPU backend for Netsaur -await setupBackend(CPU); +await setupBackend(WASM); // Create a sequential neural network const net = new Sequential({ @@ -57,6 +58,7 @@ const net = new Sequential({ ], // We are using Log Loss for finding cost cost: Cost.BinCrossEntropy, + optimizer: AdamOptimizer() }); const time = performance.now(); @@ -70,7 +72,7 @@ net.train( }, ], // Train for 150 epochs - 150, + 100, 1, // Use a smaller learning rate 0.02 diff --git a/packages/core/src/backends/wasm/backend.ts b/packages/core/src/backends/wasm/backend.ts index 921861d..2f5fa3d 100644 --- a/packages/core/src/backends/wasm/backend.ts +++ b/packages/core/src/backends/wasm/backend.ts @@ -9,6 +9,7 @@ import { wasm_backend_save, wasm_backend_train, } from "./lib/netsaur.generated.js"; +import type { PostProcessor } from "../../core/api/postprocess.ts"; /** * Web Assembly Backend. @@ -32,7 +33,7 @@ export class WASMBackend implements Backend { datasets: DataSet[], epochs: number, batches: number, - rate: number, + rate: number ): void { this.outputShape = datasets[0].outputs.shape.slice(1) as Shape; const buffer = []; @@ -52,18 +53,39 @@ export class WASMBackend implements Backend { wasm_backend_train(this.#id, buffer, options); } + async predict( + input: Tensor, + config: { postProcess: PostProcessor; outputShape?: Shape } + ): Promise>; + async predict( + input: Tensor, + config: { postProcess: PostProcessor; outputShape?: Shape }, + layers: number[] + ): Promise>; //deno-lint-ignore require-await - async predict(input: Tensor): Promise> { + async predict( + input: Tensor, + config: { postProcess: PostProcessor; outputShape?: Shape }, + layers?: number[] + ): Promise> { const options = JSON.stringify({ - inputShape: [1, ...input.shape], - outputShape: this.outputShape, + inputShape: input.shape, + outputShape: [input.shape[0], ...(config.outputShape ?? this.outputShape)], + postProcess: config.postProcess, + layers, } as PredictOptions); const output = wasm_backend_predict( this.#id, input.data as Float32Array, - options, + options + ); + return new Tensor( + output, + [ + input.shape[0], + ...(config.outputShape ?? this.outputShape), + ] as Shape, ); - return new Tensor(output, this.outputShape!); } save(): Uint8Array { diff --git a/packages/core/src/backends/wasm/lib/netsaur.generated.js b/packages/core/src/backends/wasm/lib/netsaur.generated.js index f3523ed..061600c 100644 --- a/packages/core/src/backends/wasm/lib/netsaur.generated.js +++ b/packages/core/src/backends/wasm/lib/netsaur.generated.js @@ -4,7 +4,7 @@ // deno-fmt-ignore-file /// -// source-hash: c1eff57085f8488444a8499d3d2fcad1650a7099 +// source-hash: f1db375a60100b82256510390e36e2ea79669a26 let wasm; let cachedInt32Memory0; @@ -63,6 +63,12 @@ function getStringFromWasm0(ptr, len) { return cachedTextDecoder.decode(getUint8Memory0().subarray(ptr, ptr + len)); } +function notDefined(what) { + return () => { + throw new Error(`${what} is not defined`); + }; +} + let WASM_VECTOR_LEN = 0; const cachedTextEncoder = typeof TextEncoder !== "undefined" @@ -220,6 +226,9 @@ const imports = { __wbg_log_6f7dfa87fad40a57: function (arg0, arg1) { console.log(getStringFromWasm0(arg0, arg1)); }, + __wbg_now_de5fe0de473bcd7d: typeof Date.now == "function" + ? Date.now + : notDefined("Date.now"), __wbindgen_number_new: function (arg0) { const ret = arg0; return addHeapObject(ret); @@ -249,6 +258,10 @@ const imports = { const ret = typeof (getObject(arg0)) === "string"; return ret; }, + __wbg_msCrypto_bcb970640f50a1e8: function (arg0) { + const ret = getObject(arg0).msCrypto; + return addHeapObject(ret); + }, __wbg_require_8f08ceecec0f4fee: function () { return handleError(function () { const ret = module.require; @@ -263,10 +276,6 @@ const imports = { const ret = getStringFromWasm0(arg0, arg1); return addHeapObject(ret); }, - __wbg_msCrypto_bcb970640f50a1e8: function (arg0) { - const ret = getObject(arg0).msCrypto; - return addHeapObject(ret); - }, __wbg_randomFillSync_dc1e9a60c158336d: function () { return handleError(function (arg0, arg1) { getObject(arg0).randomFillSync(takeObject(arg1)); diff --git a/packages/core/src/backends/wasm/lib/netsaur_bg.wasm b/packages/core/src/backends/wasm/lib/netsaur_bg.wasm index 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