-
-
Notifications
You must be signed in to change notification settings - Fork 99
/
gradients.rs
335 lines (303 loc) · 11.2 KB
/
gradients.rs
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
//! Implementations of [OwnedTape], [NoneTape], and generic Nd array containers via [Gradients].
#![allow(clippy::type_complexity)]
use std::collections::{BTreeMap, BTreeSet};
use std::{boxed::Box, vec::Vec};
use super::tensorlike::Tensorlike;
use super::{storage_traits::Storage, unique_id, Tensor, UniqueId};
use crate::shapes::Shape;
/// A generic container for keeping gradients of tensors keyed by the
/// tensor's [UniqueId].
///
/// You can:
/// 1. Insert array values into it
/// 2. Remove entries
/// 3. Access references to arrays
/// 4. Access mutable references to arrays
#[derive(Clone, Debug)]
pub struct Gradients<E, D: Storage<E>> {
/// Using BTreeMap for no-std support
gradient_by_id: BTreeMap<UniqueId, D::Vec>,
/// Using BTreeSet for no-std support
leaf_ids: Option<BTreeSet<UniqueId>>,
}
impl<E, D: Storage<E>> Gradients<E, D> {
/// Creates a [Gradients] object without any leaf tensor ids.
/// **This will never drop gradients for temporary tensors**.
///
/// This is why this method is called `leaky`, because
/// it will keep gradients from previous passes if it is
/// used consecutively.
///
/// **You should use [crate::nn::ZeroGrads::alloc_grads]**,
/// which will ensure non-leaf gradients are freed after backwards.
pub fn leaky() -> Self {
Self {
gradient_by_id: Default::default(),
leaf_ids: None,
}
}
}
impl<E, D: Storage<E>> Gradients<E, D> {
/// Retrieves mutable gradient for `t`, allocating one if it isn't present.
pub fn get_or_alloc_mut<S: Shape>(
&mut self,
t: &impl Tensorlike<S, E, D>,
) -> Result<&mut D::Vec, D::Err> {
self.try_alloc_for(t)?;
Ok(self.get_mut(t))
}
/// Inserts a gradient for `t`
pub fn try_alloc_for<S: Shape>(&mut self, t: &impl Tensorlike<S, E, D>) -> Result<(), D::Err> {
if let std::collections::btree_map::Entry::Vacant(e) = self.gradient_by_id.entry(t.id()) {
e.insert(t.try_alloc_grad()?);
}
Ok(())
}
/// Drops all gradients except for the ids specified in the parameter.
pub fn retain_leafs(&mut self, ids: &[UniqueId]) {
self.leaf_ids
.get_or_insert_with(Default::default)
.extend(ids);
self.drop_non_leafs();
}
/// Keeps all gradients marked previously by [Gradients::retain_leafs], and drops all
/// others.
pub fn drop_non_leafs(&mut self) {
if let Some(leafs) = &self.leaf_ids {
self.gradient_by_id.retain(|k, _| leafs.contains(k));
}
}
/// Returns a reference to the underlying gradient if found.
pub(crate) fn get_ref_checked<S: Shape, T>(&self, t: &Tensor<S, E, D, T>) -> Option<&D::Vec> {
self.gradient_by_id.get(&t.id)
}
/// Returns a mutable reference to the data associated with `t`.
///
/// **Panics** if data associated with `t` is not found. This indicates an unrecoverable bug.
pub(crate) fn get_mut<S: Shape>(&mut self, t: &impl Tensorlike<S, E, D>) -> &mut D::Vec {
self.gradient_by_id.get_mut(&t.id()).unwrap()
}
/// Returns an immutable reference to the data associated with `t`.
///
/// **Panics** if data associated with `t` is not found. This indicates an unrecoverable bug.
pub(crate) fn get_ref<S: Shape>(&mut self, t: &impl Tensorlike<S, E, D>) -> &D::Vec {
self.gradient_by_id.get(&t.id()).unwrap()
}
/// Clones the gradient and transforms it into a tensor.
///
/// # Panics
/// If no data is associated with `t` yet, this will panic due to an unwrap()
/// on a .get() to the underlying hashmap.
pub fn get<S: Shape>(&self, t: &impl Tensorlike<S, E, D>) -> Tensor<S, E, D> {
let buf = self.gradient_by_id.get(&t.id()).unwrap().clone();
Tensor {
id: unique_id(),
data: std::sync::Arc::new(buf),
shape: *t.shape(),
strides: t.strides(),
device: t.dev().clone(),
tape: Default::default(),
}
}
/// Borrows a pair of a gradients `(&mut L, &R)`.
/// `l` is the gradient to update, and `r` is the gradient to backprop.
///
/// **Panics** if `l` and `r` have the same id.
pub(crate) fn mut_and_ref<L: Shape, R: Shape>(
&mut self,
l: &impl Tensorlike<L, E, D>,
r: &impl Tensorlike<R, E, D>,
) -> (&mut D::Vec, &D::Vec) {
assert_ne!(l.id(), r.id());
let l_ptr = self.get_mut(l) as *mut _;
let r_ptr = self.get_ref(r) as *const _;
let l_ref = unsafe { &mut *l_ptr };
let r_ref = unsafe { &*r_ptr };
(l_ref, r_ref)
}
/// Borrows a triplet of gradients `(&mut L1, &mut L2, &R)`.
pub(crate) fn muts_and_ref<L1: Shape, L2: Shape, R: Shape>(
&mut self,
l1: &impl Tensorlike<L1, E, D>,
l2: &impl Tensorlike<L2, E, D>,
r: &impl Tensorlike<R, E, D>,
) -> (&mut D::Vec, &mut D::Vec, &D::Vec) {
assert_ne!(l1.id(), l2.id());
assert_ne!(l1.id(), r.id());
assert_ne!(l2.id(), r.id());
let l1_ptr = self.get_mut(l1) as *mut _;
let l2_ptr = self.get_mut(l2) as *mut _;
let r_ptr = self.get_ref(r) as *const _;
let l1_ref = unsafe { &mut *l1_ptr };
let l2_ref = unsafe { &mut *l2_ptr };
let r_ref = unsafe { &*r_ptr };
(l1_ref, l2_ref, r_ref)
}
#[inline]
pub(crate) fn many_and_ref<L: Shape, R: Shape>(
&mut self,
ls: &Vec<impl Tensorlike<L, E, D>>,
r: &impl Tensorlike<R, E, D>,
) -> (Vec<&mut D::Vec>, &D::Vec) {
for i in 0..ls.len() {
assert_ne!(ls[i].id(), r.id());
for j in (i + 1)..ls.len() {
assert_ne!(ls[i].id(), ls[j].id());
}
}
let l_refs: Vec<&mut D::Vec> = ls
.iter()
.map(|l| {
let l_ptr = self.get_mut(l) as *mut D::Vec;
unsafe { &mut *l_ptr }
})
.collect();
let r_ptr = self.get_ref(r) as *const _;
let r_ref = unsafe { &*r_ptr };
(l_refs, r_ref)
}
}
/// Contains a [Gradients] and list of backward operations.
pub struct OwnedTape<E, D: Storage<E>> {
/// A list of (Time, BackwardOp) pairs. The Time is used to ensure operations
/// from merged tapes are executed in the correct order.
pub(crate) operations: Vec<(UniqueId, BackwardOp<E, D, D::Err>)>,
pub(crate) gradients: Gradients<E, D>,
}
impl<E, D: Storage<E>> Default for OwnedTape<E, D> {
fn default() -> Self {
Self {
operations: Default::default(),
gradients: Gradients::leaky(),
}
}
}
impl<E: std::fmt::Debug, D: Storage<E>> std::fmt::Debug for OwnedTape<E, D> {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("OwnedTape")
.field("num_operations", &self.operations.len())
.field("gradients", &self.gradients)
.finish()
}
}
impl<E, D: Storage<E>> From<Gradients<E, D>> for OwnedTape<E, D> {
fn from(gradients: Gradients<E, D>) -> Self {
Self {
operations: Default::default(),
gradients,
}
}
}
impl<E, D: Storage<E>> OwnedTape<E, D> {
/// Compute the [Gradients]! This just runs all the operations on a new [Gradients] struct.
///
/// Note that this method takes ownership of self, so it can't be called twice!
pub(crate) fn execute(&mut self) -> Result<Gradients<E, D>, D::Err> {
// We must ensure that the operations are sorted in execution time order.
// Otherwise an backward operation may not be executed in the right order
// if multiple tapes were merged together.
self.operations.sort_by_key(|(k, _)| *k);
// In case the same operation is present multiple times, we dedup it.
self.operations.dedup_by_key(|(k, _)| *k);
for (_, operation) in self.operations.drain(..).rev() {
(operation)(&mut self.gradients)?;
}
Ok(std::mem::replace(&mut self.gradients, Gradients::leaky()))
}
}
type BackwardOp<E, D, Err> = Box<dyn FnOnce(&mut Gradients<E, D>) -> Result<(), Err>>;
/// Contains nothing. When [Tape::add_backward_op] is called, this struct does nothing.
#[derive(Default, Debug, Clone, Copy)]
pub struct NoneTape;
/// Something that can track backward operations.
pub trait Tape<E, D: Storage<E>>: Default + Merge<Self> + Merge<NoneTape> {
/// Whether this object is currently tracking gradients. This is known at compile time.
const OWNS_TAPE: bool;
fn add_backward_op<F>(&mut self, operation: F)
where
F: 'static + FnOnce(&mut Gradients<E, D>) -> Result<(), D::Err>;
}
impl<E, D: Storage<E>> Tape<E, D> for OwnedTape<E, D> {
const OWNS_TAPE: bool = true;
fn add_backward_op<F>(&mut self, operation: F)
where
F: 'static + FnOnce(&mut Gradients<E, D>) -> Result<(), D::Err>,
{
self.operations.push((unique_id(), Box::new(operation)));
}
}
impl<E, D: Storage<E>> Tape<E, D> for NoneTape {
const OWNS_TAPE: bool = false;
fn add_backward_op<F>(&mut self, _: F)
where
F: 'static + FnOnce(&mut Gradients<E, D>) -> Result<(), D::Err>,
{
}
}
/// Combine two things
pub trait Merge<T: ?Sized> {
/// Merges `T` into `self`
fn merge(self, other: T) -> Self;
}
impl Merge<NoneTape> for NoneTape {
fn merge(self, _: NoneTape) -> Self {
self
}
}
impl<E, D: Storage<E>> Merge<NoneTape> for OwnedTape<E, D> {
fn merge(self, _: NoneTape) -> Self {
self
}
}
impl<E, D: Storage<E>> Merge<OwnedTape<E, D>> for OwnedTape<E, D> {
fn merge(mut self, mut other: Self) -> Self {
self.gradients
.gradient_by_id
.extend(other.gradients.gradient_by_id);
if let Some(leafs) = other.gradients.leaf_ids {
self.gradients
.leaf_ids
.get_or_insert_with(Default::default)
.extend(leafs);
}
self.operations.append(&mut other.operations);
self
}
}
#[cfg(feature = "std")]
impl<E, D: Storage<E>> Merge<NoneTape> for std::sync::Arc<std::sync::Mutex<OwnedTape<E, D>>> {
fn merge(self, _: NoneTape) -> Self {
self
}
}
#[cfg(feature = "std")]
impl<E, D: Storage<E>> Merge<Self> for std::sync::Arc<std::sync::Mutex<OwnedTape<E, D>>> {
fn merge(self, other: Self) -> Self {
if !std::sync::Arc::ptr_eq(&self, &other) {
let mut lhs = self.lock().unwrap();
let mut rhs = other.lock().unwrap();
lhs.gradients
.gradient_by_id
.append(&mut rhs.gradients.gradient_by_id);
if let Some(leafs) = &mut rhs.gradients.leaf_ids {
lhs.gradients
.leaf_ids
.get_or_insert_with(Default::default)
.append(leafs);
}
lhs.operations.append(&mut rhs.operations);
}
self
}
}
#[cfg(feature = "std")]
impl<E, D: Storage<E>> Tape<E, D> for std::sync::Arc<std::sync::Mutex<OwnedTape<E, D>>> {
const OWNS_TAPE: bool = true;
fn add_backward_op<F>(&mut self, operation: F)
where
F: 'static + FnOnce(&mut Gradients<E, D>) -> Result<(), D::Err>,
{
let mut tape = self.lock().unwrap();
tape.add_backward_op(operation);
}
}