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use co::prelude::*;
use layers::*;
use weight::WeightConfig;
use util::{ArcLock, native_backend, LayerOps};
use std::fmt;
use std::cmp;
use std::collections::{HashMap, HashSet};
use std::fs::File;
use std::io::{self, BufReader};
use std::path::Path;
use std::rc::Rc;
use std::sync::{Arc, RwLock};
use leaf_capnp::layer as capnp_layer;
use leaf_capnp::layer_config as capnp_layer_config;
use leaf_capnp::layer_config::layer_type as capnp_layer_type;
use capnp_util::*;
#[derive(Debug)]
pub struct Layer<B: IBackend> {
pub name: String,
pub config: Box<LayerConfig>,
pub worker: Box<ILayer<B>>,
backend: Rc<B>,
needs_backward: bool,
pub weights_data: Vec<ArcLock<SharedTensor<f32>>>,
pub weights_gradient: Vec<ArcLock<SharedTensor<f32>>>,
learnable_weights: Vec<ArcLock<SharedTensor<f32>>>,
weights_lr: Vec<Option<f32>>,
weights_weight_decay: Vec<Option<f32>>,
weights_display_names: Vec<String>,
weight_propagate_down: Vec<bool>,
pub input_blobs_data: Vec<ArcLock<SharedTensor<f32>>>,
pub input_blobs_gradient: Vec<ArcLock<SharedTensor<f32>>>,
pub input_blob_names: Vec<String>,
input_need_backwards: Vec<bool>,
pub output_blobs_data: Vec<ArcLock<SharedTensor<f32>>>,
pub output_blobs_gradient: Vec<ArcLock<SharedTensor<f32>>>,
output_blob_names: Vec<String>,
loss: Vec<f32>,
pub blob_names: HashMap<String, (ArcLock<SharedTensor<f32>>, ArcLock<SharedTensor<f32>>)>,
}
impl<B: IBackend> Layer<B> {
pub fn connect(
&mut self,
registry: &mut HashMap<String, (ArcLock<SharedTensor<f32>>, ArcLock<SharedTensor<f32>>)>,
weight_registry: &mut HashMap<String, (ArcLock<SharedTensor<f32>>, ArcLock<SharedTensor<f32>>, Option<f32>, Option<f32>)>) {
for input_name in &self.config.inputs.clone() {
self.connect_input(input_name, registry)
}
for (output_id, _) in self.config.outputs.clone().iter().rev().enumerate() {
self.append_output(output_id, registry);
}
let config = self.config.clone();
for (output_id, _) in self.config.outputs.clone().iter().rev().enumerate() {
self.append_weight(&config, weight_registry, 0, output_id);
}
let auto_output_blobs = self.worker.auto_output_blobs();
debug!("Layer {} - auto_output_blobs: {}", &self.name, &auto_output_blobs);
let min_output_blobs = self.worker.min_output_blobs();
let exact_num_output_blobs = self.worker.exact_num_output_blobs().unwrap_or(0);
if auto_output_blobs {
let needed_num_outputs = cmp::max(min_output_blobs, exact_num_output_blobs);
for _ in 0..(needed_num_outputs - self.output_blobs_data.len()) {
info!("Adding anonymous output blob for layer {}", &self.name);
self.create_anonymous_output();
}
}
self.worker.init(self.backend.clone());
self.reshape();
self.worker.resize_shared_workspace(self.backend.clone(), None);
for t in &self.output_blobs_data {
debug!("Layer {} - output shape: {:?}", self.name, t.read().unwrap().desc());
}
}
fn connect_input(&mut self, blob_name: &str, available_blobs: &mut HashMap<String, (ArcLock<SharedTensor<f32>>, ArcLock<SharedTensor<f32>>)>) {
let input_id = self.config.inputs.iter().position(|input_name| input_name == blob_name).unwrap();
if !available_blobs.contains_key(&*blob_name) {
error!("Unknown input blob {} (layer '{}', input_id: {})",
blob_name,
self.name,
input_id);
}
info!("Input {:<15} -> Layer {:>15}", blob_name, self.name);
self.input_blob_names.push(blob_name.to_owned());
self.input_blobs_data.push(available_blobs.get(&*blob_name).expect(&format!("Unknown blob name {}", blob_name)).0.clone());
self.input_blobs_gradient.push(available_blobs.get(&*blob_name).expect(&format!("Unknown blob name {}", blob_name)).1.clone());
let mut propagate_down = true;
if !self.config.propagate_down.is_empty() {
propagate_down = self.config.propagate_down[input_id];
}
let need_backward = propagate_down;
self.input_need_backwards.push(need_backward);
}
fn append_output(&mut self,
output_id: usize,
registry: &mut HashMap<String, (ArcLock<SharedTensor<f32>>, ArcLock<SharedTensor<f32>>)>) {
let layer_config = &self.config;
let blob_name = layer_config.output(output_id).unwrap().clone();
let blob_data: ArcLock<SharedTensor<f32>>;
let blob_gradient: ArcLock<SharedTensor<f32>>;
if layer_config.input(output_id).is_some() && *layer_config.input(output_id).unwrap() == blob_name {
info!("Layer {:<15} -> Output {:>15} (in-place)", layer_config.name, blob_name);
blob_data = registry[&blob_name].0.clone();
blob_gradient = registry[&blob_name].1.clone();
} else if registry.contains_key(&blob_name) {
error!("Top blob {} produced by multiple sources.", blob_name);
return
} else {
{
info!("Layer {:<15} -> Output {:>15}", self.name, blob_name);
info!("Output {} = {}", output_id, blob_name);
}
let backend: Rc<IBackend<F=B::F>> = self.backend.clone();
blob_data = Arc::new(RwLock::new(SharedTensor::new(backend.device(), &vec![1,1,1]).unwrap()));
blob_gradient = Arc::new(RwLock::new(SharedTensor::new(backend.device(), &vec![1,1,1]).unwrap()));
}
self.output_blob_names.push(blob_name.clone());
self.output_blobs_data.push(blob_data.clone());
self.output_blobs_gradient.push(blob_gradient.clone());
self.blob_names.insert(blob_name.clone(), (blob_data.clone(), blob_gradient.clone()));
registry.insert(blob_name.clone(), (blob_data.clone(), blob_gradient.clone()));
}
fn create_anonymous_output(&mut self) {
let blob_name = "(automatic)".to_owned();
info!("{} -> {}", self.name, blob_name);
let backend: Rc<IBackend<F=B::F>> = self.backend.clone();
let output_data = Arc::new(RwLock::new(SharedTensor::new(backend.device(), &vec![1,1,1]).unwrap()));
let output_gradient = Arc::new(RwLock::new(SharedTensor::new(backend.device(), &vec![1,1,1]).unwrap()));
self.output_blobs_data.push(output_data);
self.output_blobs_gradient.push(output_gradient);
}
fn append_weight(&mut self, layer_config: &LayerConfig, registry: &mut HashMap<String, (ArcLock<SharedTensor<f32>>, ArcLock<SharedTensor<f32>>, Option<f32>, Option<f32>)>, layer_id: usize, weight_id: usize) {
if self.worker.auto_weight_blobs() {
info!("Layer {} - appending weight", &layer_config.name);
let weights_len = self.weights_data.len();
let weight_name = if weights_len > weight_id {
layer_config.param(weight_id).unwrap().name.clone()
} else {
"".to_owned()
};
let display_name = if !weight_name.is_empty() {
weight_name.clone()
} else {
format!("{}-{}", self.name, weight_id)
};
self.weights_display_names.push(display_name.clone());
let registry_name = format!("SHARED_WEIGHT_{}", display_name);
let net_weight_id = weights_len;
let output_data = self.output_blobs_data[weight_id].read().unwrap();
debug!("Layer {} - creating weight and gradient of size {:?}", &layer_config.name, output_data.desc());
let weight_data = Arc::new(RwLock::new(SharedTensor::<f32>::new(output_data.latest_device(), output_data.desc()).unwrap()));
let weight_gradient = Arc::new(RwLock::new(SharedTensor::<f32>::new(output_data.latest_device(), output_data.desc()).unwrap()));
self.weights_data.push(weight_data.clone());
self.weights_gradient.push(weight_gradient.clone());
let mut weight_config = &WeightConfig::default();
if layer_config.params_len() > weight_id {
weight_config = layer_config.param(weight_id).unwrap();
}
if weight_name.is_empty() || !registry.contains_key(®istry_name) {
if !weight_name.is_empty() {
registry.insert(weight_name.clone(),
(weight_data.clone(), weight_gradient.clone(), weight_config.lr_mult, weight_config.decay_mult));
}
let learnable_weight_id = self.learnable_weights.len();
self.learnable_weights.push(weight_data.clone());
self.weights_lr.push(weight_config.lr_mult);
self.weights_weight_decay.push(weight_config.decay_mult);
} else {
let (shared_weight_data, shared_weight_gradient, shared_lr, shared_decay_mult) = registry.get(®istry_name).unwrap().clone();
info!("Sharing weight blob '{}'", weight_name.clone());
if let Some(lr_mult) = weight_config.lr_mult {
if let Some(owner_lr_mult) = shared_lr {
if !lr_mult.eq(&owner_lr_mult) {
error!("Shared param '{}' has mismatched lr_mult.",
weight_name.clone());
}
} else {
registry.remove(®istry_name).unwrap();
registry.insert(registry_name.clone(), (shared_weight_data.clone(), shared_weight_gradient.clone(), weight_config.lr_mult, shared_decay_mult));
}
}
if let Some(decay_mult) = weight_config.decay_mult {
if let Some(owner_decay_mult) = shared_decay_mult {
if !decay_mult.eq(&owner_decay_mult) {
error!("Shared param '{}' has mismatched decay_mult.",
weight_name.clone());
}
} else {
registry.remove(®istry_name).unwrap();
registry.insert(registry_name, (shared_weight_data.clone(), shared_weight_gradient.clone(), shared_lr, weight_config.decay_mult));
}
}
}
}
}
fn reshape(&mut self) {
match self.is_using_in_place() {
false => {
self.worker.reshape(self.backend.clone(),
&mut self.input_blobs_data,
&mut self.input_blobs_gradient,
&mut self.weights_data,
&mut self.weights_gradient,
&mut self.output_blobs_data,
&mut self.output_blobs_gradient);
},
true => {
self.worker.reshape(self.backend.clone(),
&mut vec![],
&mut vec![],
&mut self.weights_data,
&mut self.weights_gradient,
&mut self.output_blobs_data,
&mut self.output_blobs_gradient);
},
}
}
pub fn init_backprop(&mut self,
blobs_under_loss: &mut HashSet<String>,
blobs_skip_backp: &mut HashSet<String>) {
let mut layer_contributes_loss = false;
let mut layer_skip_propagate_down = true;
for (output_id, _) in self.output_blobs_data.iter().enumerate() {
let blob_name = self.output_blob_names.get(output_id);
if self.loss(output_id).is_some() || blob_name.is_some() && blobs_under_loss.contains(blob_name.unwrap()) {
layer_contributes_loss = true;
}
if blob_name.is_none() || blob_name.is_some() && !blobs_skip_backp.contains(blob_name.unwrap()) {
layer_skip_propagate_down = false;
}
if layer_contributes_loss && !layer_skip_propagate_down {
break;
}
}
if self.needs_backward && layer_skip_propagate_down {
self.needs_backward = false;
for (input_id, _) in self.input_blobs_data.iter().enumerate() {
self.input_need_backwards[input_id] = false;
}
}
if !layer_contributes_loss {
self.needs_backward = false;
}
{
info!("{} needs backward computation: {}",
self.name,
self.needs_backward);
}
for (input_id, input_name) in self.input_blob_names.iter().enumerate() {
if layer_contributes_loss {
blobs_under_loss.insert(input_name.clone());
} else {
self.input_need_backwards[input_id] = false;
}
if !self.input_need_backwards[input_id] {
blobs_skip_backp.insert(input_name.clone());
}
}
}
pub fn init_force_backward(&mut self) {
self.needs_backward = true;
for (input_id, _) in self.input_need_backwards.clone().iter().enumerate() {
self.input_need_backwards[input_id] =
*self.input_need_backwards
.get(input_id)
.unwrap_or(&self.worker.allow_force_backward(input_id));
}
for (weight_id, _) in self.weights_data.clone().iter().enumerate() {
self.set_weight_propagate_down(weight_id, true);
}
}
fn expose_inputs(&mut self) {
if let Some(inputs) = self.worker.inputs_data() {
self.input_blobs_data = inputs;
}
if let Some(gradients) = self.worker.inputs_gradients() {
self.input_blobs_gradient = gradients;
}
}
fn expose_outputs(&mut self) {
if let Some(outputs) = self.worker.outputs_data() {
self.output_blobs_data = outputs;
}
if let Some(gradients) = self.worker.outputs_gradients() {
self.output_blobs_gradient = gradients;
}
}
pub fn forward(&mut self, inputs: &[ArcLock<SharedTensor<f32>>]) -> Vec<ArcLock<SharedTensor<f32>>> {
debug!("LAYER: {:?}", &self.name);
for (input_i, input) in inputs.iter().enumerate() {
let reshaped_shape = self.input_blobs_data[input_i].read().unwrap().desc().clone();
self.input_blobs_data[input_i] = input.clone();
let old_shape = self.input_blobs_data[input_i].read().unwrap().desc().clone();
if old_shape.size() != reshaped_shape.size() {
panic!("The provided input does not have the expected shape of {:?}", reshaped_shape);
}
self.input_blobs_data[input_i].write().unwrap().reshape(&reshaped_shape).unwrap();
}
self.worker.sync(&self.backend,
&mut self.input_blobs_data, &mut self.input_blobs_gradient,
&mut self.weights_data, &mut self.weights_gradient,
&mut self.output_blobs_data, &mut self.output_blobs_gradient);
let forward_time = timeit_loops!(1, {
if self.is_using_in_place() {
self.worker.forward(&self.backend, &vec![], &self.weights_data, &mut self.output_blobs_data);
} else {
self.worker.forward(&self.backend, &self.input_blobs_data, &self.weights_data, &mut self.output_blobs_data);
}
});
debug!("{:<15} - Forward time: {:.5} ms", &self.name, forward_time / 0.001);
self.output_blobs_data.clone()
}
pub fn backward(&mut self, output_gradients: &[ArcLock<SharedTensor<f32>>]) -> Vec<ArcLock<SharedTensor<f32>>> {
if self.needs_backward {
let input_gradients = self.backward_input(output_gradients);
self.backward_parameters();
input_gradients
} else {
vec![]
}
}
pub fn backward_input(&mut self, output_gradients: &[ArcLock<SharedTensor<f32>>]) -> Vec<ArcLock<SharedTensor<f32>>> {
for (output_i, output) in output_gradients.iter().enumerate() {
self.output_blobs_gradient[output_i] = output.clone();
}
self.worker.sync(&self.backend,
&mut self.input_blobs_data, &mut self.input_blobs_gradient,
&mut self.weights_data, &mut self.weights_gradient,
&mut self.output_blobs_data, &mut self.output_blobs_gradient);
if self.is_using_in_place() {
self.worker.backward_input(&self.backend,
&self.weights_data,
&vec![],
&vec![],
&self.input_blobs_data,
&mut self.input_blobs_gradient)
} else {
self.worker.backward_input(&self.backend,
&self.weights_data,
&self.output_blobs_data,
&self.output_blobs_gradient,
&self.input_blobs_data,
&mut self.input_blobs_gradient)
}
self.input_blobs_gradient.clone()
}
pub fn backward_parameters(&mut self) {
self.worker.sync(&self.backend,
&mut self.input_blobs_data, &mut self.input_blobs_gradient,
&mut self.weights_data, &mut self.weights_gradient,
&mut self.output_blobs_data, &mut self.output_blobs_gradient);
self.worker.backward_parameters(&self.backend,
&self.output_blobs_data,
&self.output_blobs_gradient,
&self.input_blobs_data,
&mut self.weights_gradient)
}
pub fn synchronize(&self) {
self.backend.synchronize().unwrap();
}
pub fn update_weights<SolverB: IBackend + ::util::SolverOps<f32>>(&mut self, backend: &SolverB) {
let mut shared_a = ::util::native_scalar(-1f32);
let _ = shared_a.add_device(IBackend::device(backend));
shared_a.sync(IBackend::device(backend)).unwrap();
for (weight_gradient, weight_data) in self.learnable_weights_gradients().iter().zip(&mut self.learnable_weights_data()) {
weight_gradient.write().unwrap().sync(IBackend::device(backend)).unwrap();
weight_data.write().unwrap().sync(IBackend::device(backend)).unwrap();
backend.axpy_plain(&shared_a, &weight_gradient.read().unwrap(), &mut weight_data.write().unwrap()).unwrap();
}
}
pub fn clear_weights_gradients(&mut self) {
for weight_gradient in &mut self.learnable_weights_gradients().iter() {
let filler = ::weight::FillerType::Constant {
value: 0f32
};
filler.fill(&mut weight_gradient.write().unwrap());
}
}
pub fn save<P: AsRef<Path>>(&mut self, path: P) -> io::Result<()> {
let path = path.as_ref();
let ref mut out = try!(File::create(path));
let mut message = ::capnp::message::Builder::new_default();
{
let mut layer = message.init_root::<capnp_layer::Builder>();
self.write_capnp(&mut layer);
}
::capnp::serialize_packed::write_message(out, &message).unwrap();
Ok(())
}
pub fn load<LB: IBackend + LayerOps<f32> + 'static, P: AsRef<Path>>(backend: Rc<LB>, path: P) -> io::Result<Layer<LB>> {
let path = path.as_ref();
let ref mut file = try!(File::open(path));
let mut reader = BufReader::new(file);
let message_reader = ::capnp::serialize_packed::read_message(&mut reader,
::capnp::message::ReaderOptions::new()).unwrap();
let read_layer = message_reader.get_root::<capnp_layer::Reader>().unwrap();
let name = read_layer.get_name().unwrap().to_owned();
let layer_config = LayerConfig::read_capnp(read_layer.get_config().unwrap());
let mut layer = Layer::from_config(backend, &layer_config);
layer.name = name;
let read_weights = read_layer.get_weights_data().unwrap();
let names = layer.learnable_weights_names();
let weights_data = layer.learnable_weights_data();
let native_backend = Backend::<Native>::default().unwrap();
for (i, (name, weight)) in names.iter().zip(weights_data).enumerate() {
for j in 0..read_weights.len() {
let capnp_weight = read_weights.get(i as u32);
if capnp_weight.get_name().unwrap() != name {
continue
}
let mut weight_lock = weight.write().unwrap();
weight_lock.sync(native_backend.device()).unwrap();
let capnp_tensor = capnp_weight.get_tensor().unwrap();
let mut shape = Vec::new();
let capnp_shape = capnp_tensor.get_shape().unwrap();
for k in 0..capnp_shape.len() {
shape.push(capnp_shape.get(k) as usize)
}
weight_lock.reshape(&shape).unwrap();
let mut native_slice = weight_lock.get_mut(native_backend.device()).unwrap().as_mut_native().unwrap().as_mut_slice::<f32>();
let data = capnp_tensor.get_data().unwrap();
for k in 0..data.len() {
native_slice[k as usize] = data.get(k);
}
}
}
Ok(layer)
}
pub fn set_weight_propagate_down(&mut self, weight_id: usize, value: bool) {
if self.weight_propagate_down.len() <= weight_id {
self.weight_propagate_down.resize(weight_id + 1, true);
}
self.weight_propagate_down[weight_id] = value;
}
pub fn is_using_in_place(&self) -> bool {
self.worker.compute_in_place() &&
self.input_blob_names.get(0).is_some() &&
self.output_blob_names.get(0).is_some() &&
self.input_blob_names[0] == self.output_blob_names[0]
}
pub fn input_blob_names(&self) -> &[String] {
&self.input_blob_names
}
pub fn loss(&self, weight_id: usize) -> Option<&f32> {
self.loss.get(weight_id)
}
pub fn learnable_weights_data(&self) -> Vec<ArcLock<SharedTensor<f32>>> {
if let Some(weights) = self.worker.learnable_weights() { weights }
else { self.weights_data.clone() }
}
pub fn learnable_weights_gradients(&self) -> Vec<ArcLock<SharedTensor<f32>>> {
if let Some(gradients) = self.worker.learnable_weights_gradients() { gradients }
else { self.weights_gradient.clone() }
}
pub fn learnable_weights_names(&self) -> Vec<String> {
if let Some(names) = self.worker.learnable_weights_names() { names }
else { self.weights_display_names.clone() }
}
pub fn learnable_weights_lr(&self) -> Vec<Option<f32>> {
if let Some(lr) = self.worker.learnable_weights_lr() { lr }
else {
self.learnable_weights_data().iter().map(|_| Some(1f32)).collect::<Vec<_>>() }
}
}
#[allow(unsafe_code)]
unsafe impl<B: IBackend> Send for Layer<B> {}
impl<'a, B: IBackend> CapnpWrite<'a> for Layer<B> {
type Builder = capnp_layer::Builder<'a>;
fn write_capnp(&self, builder: &mut Self::Builder) {
builder.set_name(&self.name);
{
let mut layer_config = builder.borrow().init_config();
self.config.write_capnp(&mut layer_config);
}
{
let native_backend = Backend::<Native>::default().unwrap();
let mut weights = builder.borrow().init_weights_data(self.learnable_weights_names().len() as u32);
let names = self.learnable_weights_names();
let weights_data = self.learnable_weights_data();
for (i, (name, weight)) in names.iter().zip(weights_data).enumerate() {
let mut capnp_weight = weights.borrow().get(i as u32);
capnp_weight.set_name(name);
let mut weight_lock = weight.write().unwrap();
weight_lock.sync(native_backend.device()).unwrap();
let mut tensor = capnp_weight.init_tensor();
{
let mut tensor_shape = tensor.borrow().init_shape(weight_lock.desc().len() as u32);
for (i, dim) in weight_lock.desc().iter().enumerate() {
tensor_shape.set(i as u32, *dim as u64);
}
}
{
let native_slice = weight_lock.get(native_backend.device()).unwrap().as_native().unwrap().as_slice::<f32>();
let mut tensor_data = tensor.borrow().init_data(native_slice.len() as u32);
for (i, datum) in native_slice.iter().enumerate() {
tensor_data.set(i as u32, *datum);
}
}
}
}
}
}
impl<B: IBackend + LayerOps<f32> + 'static> Layer<B> {
pub fn from_config(backend: Rc<B>, config: &LayerConfig) -> Layer<B> {
let cl = config.clone();
let cfg = Box::<LayerConfig>::new(cl);
let mut layer = Layer {
name: cfg.name.clone(),
needs_backward: true,
weights_data: Vec::new(),
weights_gradient: Vec::new(),
learnable_weights: Vec::new(),
weight_propagate_down: Vec::new(),
weights_lr: Vec::new(),
weights_weight_decay: Vec::new(),
weights_display_names: Vec::new(),
input_blobs_data: Vec::new(),
input_blobs_gradient: Vec::new(),
input_blob_names: Vec::new(),
input_need_backwards: Vec::new(),
output_blobs_data: Vec::new(),
output_blobs_gradient: Vec::new(),
output_blob_names: Vec::new(),
loss: vec![1f32, 1f32, 1f32],
blob_names: HashMap::new(),
backend: backend.clone(),
worker: Layer::<B>::worker_from_config(backend, &cfg),
config: cfg,
};
layer.expose_inputs();
layer.expose_outputs();
layer
}
fn worker_from_config(backend: Rc<B>, config: &LayerConfig) -> Box<ILayer<B>> {
match config.layer_type.clone() {
#[cfg(all(feature="cuda", not(feature="native")))]
LayerType::Convolution(layer_config) => Box::new(Convolution::from_config(&layer_config)),
LayerType::Linear(layer_config) => Box::new(Linear::from_config(&layer_config)),
LayerType::LogSoftmax => Box::new(LogSoftmax::default()),
#[cfg(all(feature="cuda", not(feature="native")))]
LayerType::Pooling(layer_config) => Box::new(Pooling::from_config(&layer_config)),
LayerType::Sequential(layer_config) => Box::new(Sequential::from_config(backend, &layer_config)),
LayerType::Softmax => Box::new(Softmax::default()),
LayerType::ReLU => Box::new(ReLU),
LayerType::Sigmoid => Box::new(Sigmoid),
LayerType::NegativeLogLikelihood(layer_config) => Box::new(NegativeLogLikelihood::from_config(&layer_config)),
LayerType::Reshape(layer_config) => Box::new(Reshape::from_config(&layer_config)),
}
}
}
pub trait ILayer<B: IBackend> : ComputeOutput<f32, B> + ComputeInputGradient<f32, B> + ComputeParametersGradient<f32, B> {
fn init(&mut self, backend: Rc<B>) {}
fn reshape(&mut self,
backend: Rc<B>,
input_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
input_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
weights_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
weights_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
output_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
output_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>) {}
fn resize_shared_workspace(&mut self, backend: Rc<B>, workspace: Option<ArcLock<SharedTensor<u8>>>) -> Option<ArcLock<SharedTensor<u8>>> {
workspace
}
#[cfg_attr(lint, allow(map_clone))]
fn forward(&self,
backend: &B,
input_data: &[ArcLock<SharedTensor<f32>>],
weights_data: &[ArcLock<SharedTensor<f32>>],
output_data: &mut [ArcLock<SharedTensor<f32>>]) {
let inp: Vec<_> = input_data.iter().map(|b| b.read().unwrap()).collect();
let input_data_: Vec<&SharedTensor<f32>> = inp.iter().enumerate().map(|(_, val)| &**val).collect();
let wgts: Vec<_> = weights_data.iter().map(|w| w.read().unwrap()).collect();
let weights_data_: Vec<&SharedTensor<f32>> = wgts.iter().enumerate().map(|(_, val)| &**val).collect();
let out_ref = output_data.iter().cloned().collect::<Vec<_>>();
let mut out = &mut out_ref.iter().map(|b| b.write().unwrap()).collect::<Vec<_>>();
let mut output_w = &mut out.iter_mut().map(|a| a).collect::<Vec<_>>();
let mut output_data_: Vec<&mut SharedTensor<f32>> = output_w.iter_mut().enumerate().map(|(_, val)| &mut ***val).collect();
self.compute_output(backend, &weights_data_, &input_data_, &mut output_data_);
}
#[cfg_attr(lint, allow(map_clone))]
fn backward_input(&self,
backend: &B,
weights_data: &[ArcLock<SharedTensor<f32>>],
output_data: &[ArcLock<SharedTensor<f32>>],
output_gradients: &[ArcLock<SharedTensor<f32>>],
input_data: &[ArcLock<SharedTensor<f32>>],
input_gradients: &mut [ArcLock<SharedTensor<f32>>]) {
let wgts_data: Vec<_> = weights_data.iter().map(|b| b.read().unwrap()).collect();
let weights_data_: Vec<&SharedTensor<f32>> = wgts_data.iter().enumerate().map(|(_, val)| &**val).collect();
let out_data: Vec<_> = output_data.iter().map(|b| b.read().unwrap()).collect();
let output_data_: Vec<&SharedTensor<f32>> = out_data.iter().enumerate().map(|(_, val)| &**val).collect();
let out_gradient: Vec<_> = output_gradients.iter().map(|b| b.read().unwrap()).collect();
let output_gradients_: Vec<&SharedTensor<f32>> = out_gradient.iter().enumerate().map(|(_, val)| &**val).collect();
let inp_data: Vec<_> = input_data.iter().map(|b| b.read().unwrap()).collect();
let input_data_: Vec<&SharedTensor<f32>> = inp_data.iter().enumerate().map(|(_, val)| &**val).collect();
let btm_gradient_ref = input_gradients.iter().cloned().collect::<Vec<_>>();
let mut btm_gradient = &mut btm_gradient_ref.iter().map(|b| b.write().unwrap()).collect::<Vec<_>>();
let mut input_gradient = &mut btm_gradient.iter_mut().map(|a| a).collect::<Vec<_>>();
let mut input_gradients_: Vec<&mut SharedTensor<f32>> = input_gradient.iter_mut().enumerate().map(|(_, val)| &mut ***val).collect();
self.compute_input_gradient(backend, &weights_data_, &output_data_, &output_gradients_, &input_data_, &mut input_gradients_);
}
#[cfg_attr(lint, allow(map_clone))]
fn backward_parameters(&self,
backend: &B,
output_data: &[ArcLock<SharedTensor<f32>>],
output_gradients: &[ArcLock<SharedTensor<f32>>],
input_data: &[ArcLock<SharedTensor<f32>>],
weights_gradients: &mut [ArcLock<SharedTensor<f32>>]) {
let out_data: Vec<_> = output_data.iter().map(|b| b.read().unwrap()).collect();
let output_data_: Vec<&SharedTensor<f32>> = out_data.iter().enumerate().map(|(_, val)| &**val).collect();
let out_gradients: Vec<_> = output_gradients.iter().map(|b| b.read().unwrap()).collect();
let output_gradients_: Vec<&SharedTensor<f32>> = out_gradients.iter().enumerate().map(|(_, val)| &**val).collect();
let inp_data: Vec<_> = input_data.iter().map(|b| b.read().unwrap()).collect();
let input_data_: Vec<&SharedTensor<f32>> = inp_data.iter().enumerate().map(|(_, val)| &**val).collect();
let wgt_gradient_ref = weights_gradients.iter().cloned().collect::<Vec<_>>();
let mut wgt_gradient = &mut wgt_gradient_ref.iter().map(|b| b.write().unwrap()).collect::<Vec<_>>();
let mut weights_gradient = &mut wgt_gradient.iter_mut().map(|a| a).collect::<Vec<_>>();
let mut weights_gradients_: Vec<&mut SharedTensor<f32>> = weights_gradient.iter_mut().enumerate().map(|(_, val)| &mut ***val).collect();
self.compute_parameters_gradient(backend, &output_data_, &output_gradients_, &input_data_, &mut weights_gradients_);
}
fn sync(&self,
backend: &B,
input_data: &mut [ArcLock<SharedTensor<f32>>],
input_gradients: &mut [ArcLock<SharedTensor<f32>>],
weights_data: &mut [ArcLock<SharedTensor<f32>>],
weights_gradients: &mut [ArcLock<SharedTensor<f32>>],
output_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
output_gradients: &mut Vec<ArcLock<SharedTensor<f32>>>) {
if self.sync_native() {
let backend = native_backend();
for tensor in input_data {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in input_gradients {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in weights_data {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in weights_gradients {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in output_data {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in output_gradients {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
} else {
for tensor in input_data {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in input_gradients {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in weights_data {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in weights_gradients {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in output_data {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
for tensor in output_gradients {
let mut sync = tensor.write().unwrap();
match sync.add_device(backend.device()) { _ => sync.sync(backend.device()).unwrap() }
}
}
}
fn auto_output_blobs(&self) -> bool {
false
}
fn min_output_blobs(&self) -> usize {
0
}
fn exact_num_output_blobs(&self) -> Option<usize> {
None
}
fn auto_weight_blobs(&self) -> bool {
false
}
fn exact_num_input_blobs(&self) -> Option<usize> {
None
}
fn allow_force_backward(&self, input_id: usize) -> bool {
true
}
fn sync_native(&self) -> bool {
false
}
fn compute_in_place(&self) -> bool {
false
}
fn is_container(&self) -> bool {
false
}
fn loss_weight(&self, output_id: usize) -> Option<f32> {
None
}
fn inputs_data(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>> {
None
}
fn inputs_gradients(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>> {
None
}
fn outputs_data(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>> {
None
}
fn outputs_gradients(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>> {
None
}
fn learnable_weights(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>> {
None
}
fn learnable_weights_gradients(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>> {
None
}
fn learnable_weights_names(&self) -> Option<Vec<String>> {
None
}
fn learnable_weights_lr(&self) -> Option<Vec<Option<f32>>> {
None
}
}
pub trait ComputeOutput<T, B: IBackend> {
fn compute_output(&self,
backend: &B,
weights_data: &[&SharedTensor<T>],
input_data: &[&SharedTensor<T>],
output_data: &mut [&mut SharedTensor<T>]);
}
pub trait ComputeInputGradient<T, B: IBackend> {
fn compute_input_gradient(&self,
backend: &B,
weights_data: &[&SharedTensor<T>],
output_data: &[&SharedTensor<T>],
output_gradients: &[&SharedTensor<T>],
input_data: &[&SharedTensor<T>],
input_gradients: &mut [&mut SharedTensor<T>]);
}
pub trait ComputeParametersGradient<T, B: IBackend> {
fn compute_parameters_gradient(&self,
backend: &B,
output_data: &[&SharedTensor<T>],
output_gradients: &[&SharedTensor<T>],
input_data: &[&SharedTensor<T>],
parameters_gradients: &mut [&mut SharedTensor<T>]) {}
}
impl<B: IBackend> fmt::Debug for ILayer<B> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
write!(f, "({})", "ILayer")
}
}
#[derive(Debug, Clone)]
pub struct LayerConfig {
pub name: String,
pub layer_type: LayerType,
pub outputs: Vec<String>,
pub inputs: Vec<String>,
pub params: Vec<WeightConfig>,
pub propagate_down: Vec<bool>,
}
#[derive(Debug, Clone)]
pub enum LayerType {
#[cfg(all(feature="cuda", not(feature="native")))]
Convolution(ConvolutionConfig),
Linear(LinearConfig),
LogSoftmax,
#[cfg(all(feature="cuda", not(feature="native")))]
Pooling(PoolingConfig),
Sequential(SequentialConfig),
Softmax,
ReLU,
Sigmoid,
NegativeLogLikelihood(NegativeLogLikelihoodConfig),
Reshape(ReshapeConfig),
}
impl LayerType {
pub fn supports_in_place(&self) -> bool {
match *self {
#[cfg(all(feature="cuda", not(feature="native")))]
LayerType::Convolution(_) => false,
LayerType::Linear(_) => false,
LayerType::LogSoftmax => false,
#[cfg(all(feature="cuda", not(feature="native")))]
LayerType::Pooling(_) => false,
LayerType::Sequential(_) => false,
LayerType::Softmax => false,
#[cfg(all(feature="cuda", not(feature="native")))]
LayerType::ReLU => true,
#[cfg(feature="native")]
LayerType::ReLU => false,
#[cfg(all(feature="cuda", not(feature="native")))]
LayerType::Sigmoid => true,
#[cfg(feature="native")]
LayerType::Sigmoid => false,
LayerType::NegativeLogLikelihood(_) => false,
LayerType::Reshape(_) => true,
}
}
}
impl<'a> CapnpWrite<'a> for LayerType {
type Builder = capnp_layer_type::Builder<'a>;
fn write_capnp(&self, builder: &mut Self::Builder) {
match self {
#[cfg(all(feature="cuda", not(feature="native")))]
&LayerType::Convolution(ref cfg) => { let ref mut config = builder.borrow().init_convolution(); cfg.write_capnp(config); },
&LayerType::Linear(ref cfg) => { let ref mut config = builder.borrow().init_linear(); cfg.write_capnp(config); },
&LayerType::LogSoftmax => { builder.set_log_softmax(()) },
#[cfg(all(feature="cuda", not(feature="native")))]
&LayerType::Pooling(ref cfg) => { let ref mut config = builder.borrow().init_pooling(); cfg.write_capnp(config); },
&LayerType::Sequential(ref cfg) => { let ref mut config = builder.borrow().init_sequential(); cfg.write_capnp(config); },
&LayerType::Softmax => { builder.set_softmax(()) },
#[cfg(all(feature="cuda", not(feature="native")))]
&LayerType::ReLU => { builder.set_relu(()) },
#[cfg(feature="native")]
&LayerType::ReLU => { builder.set_relu(()) },
#[cfg(all(feature="cuda", not(feature="native")))]
&LayerType::Sigmoid => { builder.set_sigmoid(()) },
#[cfg(feature="native")]
&LayerType::Sigmoid => { builder.set_sigmoid(()) },
&LayerType::NegativeLogLikelihood(ref cfg) => { let ref mut config = builder.borrow().init_negative_log_likelihood(); cfg.write_capnp(config); },
&LayerType::Reshape(ref cfg) => { let ref mut config = builder.borrow().init_reshape(); cfg.write_capnp(config); },
}
}
}
impl<'a> CapnpRead<'a> for LayerType {
type Reader = capnp_layer_type::Reader<'a>;
fn read_capnp(reader: Self::Reader) -> Self {
match reader.which().unwrap() {
#[cfg(all(feature="cuda", not(feature="native")))]
capnp_layer_type::Which::Convolution(read_config) => { let config = ConvolutionConfig::read_capnp(read_config.unwrap()); LayerType::Convolution(config) },
#[cfg(not(all(feature="cuda", not(feature="native"))))]
capnp_layer_type::Which::Convolution(_) => { panic!("Can not load Network because Convolution layer is not supported with the used feature flags.") },
capnp_layer_type::Which::Linear(read_config) => { let config = LinearConfig::read_capnp(read_config.unwrap()); LayerType::Linear(config) },
capnp_layer_type::Which::LogSoftmax(read_config) => { LayerType::LogSoftmax },
#[cfg(all(feature="cuda", not(feature="native")))]
capnp_layer_type::Which::Pooling(read_config) => { let config = PoolingConfig::read_capnp(read_config.unwrap()); LayerType::Pooling(config) },
#[cfg(not(all(feature="cuda", not(feature="native"))))]
capnp_layer_type::Which::Pooling(_) => { panic!("Can not load Network because Pooling layer is not supported with the used feature flags.") },
capnp_layer_type::Which::Sequential(read_config) => { let config = SequentialConfig::read_capnp(read_config.unwrap()); LayerType::Sequential(config) },
capnp_layer_type::Which::Softmax(_) => { LayerType::Softmax },
capnp_layer_type::Which::Relu(_) => { LayerType::ReLU },
capnp_layer_type::Which::Sigmoid(_) => { LayerType::Sigmoid },
capnp_layer_type::Which::NegativeLogLikelihood(read_config) => { let config = NegativeLogLikelihoodConfig::read_capnp(read_config.unwrap()); LayerType::NegativeLogLikelihood(config) },
capnp_layer_type::Which::Reshape(read_config) => { let config = ReshapeConfig::read_capnp(read_config.unwrap()); LayerType::Reshape(config) },
}
}
}
impl LayerConfig {
pub fn new<L: Into<LayerType>>(name: &str, layer_type: L) -> LayerConfig {
LayerConfig {
name: name.to_owned(),
layer_type: layer_type.into(),
outputs: Vec::new(),
inputs: Vec::new(),
params: Vec::new(),
propagate_down: Vec::new(),
}
}
pub fn output(&self, output_id: usize) -> Option<&String> {
self.outputs.get(output_id)
}
pub fn outputs_len(&self) -> usize {
self.outputs.len()
}
pub fn add_output(&mut self, output_name: &str) {
self.outputs.push(output_name.to_owned());
}
pub fn input(&self, input_id: usize) -> Option<&String> {
self.inputs.get(input_id)
}
pub fn inputs_len(&self) -> usize {
self.inputs.len()
}
pub fn add_input(&mut self, input_name: &str) {
self.inputs.push(input_name.to_owned());
}
pub fn param(&self, param_id: usize) -> Option<&WeightConfig> {
self.params.get(param_id)
}
pub fn params_len(&self) -> usize {
self.params.len()
}
pub fn validate(&self) -> Result<(), &'static str> {
try!(self.validate_propagate_down_len());
Ok(())
}
fn validate_propagate_down_len(&self) -> Result<(), &'static str> {
if self.propagate_down.is_empty() || self.propagate_down.len() == self.inputs_len() {
Ok(())
} else {
Err("propagate_down config must be specified either 0 or inputs_len times")
}
}
}
impl<'a> CapnpWrite<'a> for LayerConfig {
type Builder = capnp_layer_config::Builder<'a>;
fn write_capnp(&self, builder: &mut Self::Builder) {
builder.set_name(&self.name);
{
let mut layer_type = builder.borrow().init_layer_type();
self.layer_type.write_capnp(&mut layer_type);
}
{
let mut outputs = builder.borrow().init_outputs(self.outputs.len() as u32);
for (i, output) in self.outputs.iter().enumerate() {
outputs.set(i as u32, &output);
}
}
{
let mut inputs = builder.borrow().init_inputs(self.inputs.len() as u32);
for (i, input) in self.inputs.iter().enumerate() {
inputs.set(i as u32, &input);
}
}
{
let mut params = builder.borrow().init_params(self.params.len() as u32);
for (i, param) in self.params.iter().enumerate() {
let ref mut capnp_param = params.borrow().get(i as u32);
param.write_capnp(capnp_param);
}
}
{
let mut propagate_down = builder.borrow().init_propagate_down(self.propagate_down.len() as u32);
for (i, input) in self.propagate_down.iter().enumerate() {
propagate_down.set(i as u32, *input);
}
}
}
}
impl<'a> CapnpRead<'a> for LayerConfig {
type Reader = capnp_layer_config::Reader<'a>;
fn read_capnp(reader: Self::Reader) -> Self {
let name = reader.get_name().unwrap().to_owned();
let layer_type = LayerType::read_capnp(reader.get_layer_type());
let read_outputs = reader.get_outputs().unwrap();
let mut outputs = Vec::new();
for i in 0..read_outputs.len() {
outputs.push(read_outputs.get(i).unwrap().to_owned())
}
let read_inputs = reader.get_inputs().unwrap();
let mut inputs = Vec::new();
for i in 0..read_inputs.len() {
inputs.push(read_inputs.get(i).unwrap().to_owned())
}
let read_params = reader.get_params().unwrap();
let mut params = Vec::new();
for i in 0..read_params.len() {
params.push(WeightConfig::read_capnp(read_params.get(i)))
}
let read_propagate_down = reader.get_propagate_down().unwrap();
let mut propagate_down = Vec::new();
for i in 0..read_propagate_down.len() {
propagate_down.push(read_propagate_down.get(i))
}
LayerConfig {
name: name,
layer_type: layer_type,
outputs: outputs,
inputs: inputs,
params: params,
propagate_down: propagate_down,
}
}
}