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https://github.com/huggingface/candle.git
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Move the backprop bits to a separate file.
This commit is contained in:
laurent
290
src/backprop.rs
Normal file
290
src/backprop.rs
Normal file
@ -0,0 +1,290 @@
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use crate::{op::Op, Error, Result, Tensor, TensorId};
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use std::collections::HashMap;
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impl Tensor {
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/// Return all the nodes that lead to this value in a topologically sorted vec, the first
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/// elements having dependencies on the latter ones, e.g. the first element if any is the
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/// argument.
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/// This assumes that the op graph is a DAG.
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fn sorted_nodes(&self) -> Vec<&Tensor> {
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// The vec of sorted nodes is passed as an owned value rather than a mutable reference
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// to get around some lifetime limitations.
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fn walk<'a>(
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node: &'a Tensor,
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nodes: Vec<&'a Tensor>,
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already_seen: &mut HashMap<TensorId, bool>,
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) -> (bool, Vec<&'a Tensor>) {
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if let Some(&tg) = already_seen.get(&node.id()) {
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return (tg, nodes);
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}
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let mut track_grad = false;
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let mut nodes = if node.is_variable() {
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// Do not call recursively on the "leaf" nodes.
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track_grad = true;
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nodes
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} else if let Some(op) = node.op() {
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match op {
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Op::Add(lhs, rhs)
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| Op::Mul(lhs, rhs)
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| Op::Sub(lhs, rhs)
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| Op::Div(lhs, rhs)
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| Op::BroadcastAdd(lhs, rhs)
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| Op::BroadcastMul(lhs, rhs)
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| Op::BroadcastSub(lhs, rhs)
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| Op::BroadcastDiv(lhs, rhs)
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| Op::Embedding(lhs, rhs)
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| Op::Matmul(lhs, rhs) => {
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let (tg, nodes) = walk(lhs, nodes, already_seen);
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track_grad |= tg;
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let (tg, nodes) = walk(rhs, nodes, already_seen);
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track_grad |= tg;
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nodes
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}
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Op::Cat(args, _) => args.iter().fold(nodes, |nodes, arg| {
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let (tg, nodes) = walk(arg, nodes, already_seen);
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track_grad |= tg;
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nodes
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}),
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Op::Affine { arg, mul, .. } => {
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if *mul == 0. {
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nodes
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} else {
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let (tg, nodes) = walk(arg, nodes, already_seen);
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track_grad |= tg;
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nodes
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}
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}
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Op::Reshape(node)
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| Op::Broadcast(node)
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| Op::ToDType(node)
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| Op::ToDevice(node)
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| Op::Transpose(node, _, _)
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| Op::Narrow(node, _, _, _)
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| Op::Softmax(node, _)
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| Op::Sqr(node)
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| Op::Sqrt(node)
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| Op::Gelu(node)
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| Op::Exp(node)
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| Op::Log(node)
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| Op::Sin(node)
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| Op::Cos(node)
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| Op::Abs(node)
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| Op::Neg(node) => {
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let (tg, nodes) = walk(node, nodes, already_seen);
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track_grad |= tg;
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nodes
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}
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}
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} else {
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nodes
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};
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already_seen.insert(node.id(), track_grad);
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if track_grad {
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nodes.push(node);
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}
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(track_grad, nodes)
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}
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let (_tg, mut nodes) = walk(self, vec![], &mut HashMap::new());
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nodes.reverse();
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nodes
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}
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pub fn backward(&self) -> Result<GradStore> {
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let sorted_nodes = self.sorted_nodes();
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let mut grads = GradStore::new();
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grads.insert(self, self.ones_like()?);
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for node in sorted_nodes.iter() {
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if node.is_variable() {
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continue;
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}
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let grad = grads.remove(node).unwrap();
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// TODO: We should perform all these operations in place (or at least not track the
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// whole graph).
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// The only drawback would be if we wanted to support grad of grad but this is out of
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// scope.
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if let Some(op) = node.op() {
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match op {
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Op::Add(lhs, rhs) => {
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.add(&grad)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.add(&grad)?;
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}
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Op::Sub(lhs, rhs) => {
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.add(&grad)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.sub(&grad)?;
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}
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Op::Mul(lhs, rhs) => {
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let lhs_grad = grad.mul(rhs)?;
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
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let rhs_grad = grad.mul(lhs)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
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}
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Op::Div(lhs, rhs) => {
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let lhs_grad = grad.div(rhs)?;
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
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let rhs_grad = grad.mul(lhs)?.div(&rhs.sqr()?)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
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}
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Op::BroadcastAdd(lhs, rhs) => {
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.broadcast_add(&grad)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.broadcast_add(&grad)?;
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}
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Op::BroadcastSub(lhs, rhs) => {
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.broadcast_add(&grad)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.broadcast_sub(&grad)?;
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}
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Op::BroadcastMul(lhs, rhs) => {
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let lhs_grad = grad.broadcast_mul(rhs)?;
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.broadcast_add(&lhs_grad)?;
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let rhs_grad = grad.broadcast_mul(lhs)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.broadcast_add(&rhs_grad)?;
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}
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Op::BroadcastDiv(lhs, rhs) => {
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let lhs_grad = grad.broadcast_div(rhs)?;
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.broadcast_add(&lhs_grad)?;
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let rhs_grad = grad.broadcast_mul(lhs)?.broadcast_div(&rhs.sqr()?)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.broadcast_add(&rhs_grad)?;
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}
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Op::Embedding(_lhs, _rhs) => {
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return Err(Error::BackwardNotSupported { op: "embedding" })
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}
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Op::Matmul(lhs, rhs) => {
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// Skipping checks, the op went ok, we can skip
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// the matmul size checks for now.
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let lhs_grad = grad.matmul(&rhs.t()?)?;
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let lhs_sum_grad = grads.or_insert(lhs)?;
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*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
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let rhs_grad = lhs.t()?.matmul(&grad)?;
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let rhs_sum_grad = grads.or_insert(rhs)?;
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*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
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}
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Op::Cat(args, dim) => {
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let mut start_idx = 0;
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for arg in args {
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let len = arg.dims()[*dim];
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let arg_grad = grad.narrow(*dim, start_idx, len)?;
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&arg_grad)?;
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start_idx += len;
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}
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}
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Op::Broadcast(_arg) => {
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return Err(Error::BackwardNotSupported { op: "broadcast" })
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}
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Op::ToDType(arg) => {
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&grad.to_dtype(node.dtype())?)?
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}
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Op::Affine { arg, mul, .. } => {
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let arg_grad = grad.affine(*mul, 0.)?;
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&arg_grad)?
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}
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Op::Log(arg) => {
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&(&grad * *node)?)?
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}
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Op::Sin(arg) => {
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&(&grad * arg.cos())?)?
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}
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Op::Cos(arg) => {
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.sub(&(&grad * arg.sin())?)?
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}
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Op::Abs(_args) => return Err(Error::BackwardNotSupported { op: "abs" }),
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Op::Exp(arg) => {
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&(&grad / arg)?)?
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}
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Op::Neg(arg) => {
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.sub(&grad)?
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}
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Op::Narrow(_arg, _, _, _) => {
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return Err(Error::BackwardNotSupported { op: "narrow" })
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}
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Op::Softmax(_arg, _) => {
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return Err(Error::BackwardNotSupported { op: "softmax" })
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}
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Op::Reshape(_arg) => return Err(Error::BackwardNotSupported { op: "reshape" }),
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Op::Gelu(_) => return Err(Error::BackwardNotSupported { op: "gelu" }),
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Op::Sqr(arg) => {
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let arg_grad = arg.mul(&grad)?.affine(2., 0.)?;
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&arg_grad)?
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}
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Op::Sqrt(arg) => {
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let arg_grad = grad.div(arg)?.affine(0.5, 0.)?;
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&arg_grad)?
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}
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Op::ToDevice(arg) => {
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let sum_grad = grads.or_insert(arg)?;
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let arg_grad = grad.to_device(&sum_grad.device())?;
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*sum_grad = sum_grad.add(&arg_grad)?
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}
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Op::Transpose(arg, dim1, dim2) => {
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let arg_grad = grad.transpose(*dim1, *dim2)?;
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&arg_grad)?
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}
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};
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}
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}
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Ok(grads)
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}
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}
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pub struct GradStore(HashMap<TensorId, Tensor>);
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impl GradStore {
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fn new() -> Self {
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GradStore(HashMap::new())
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}
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pub fn get_id(&self, id: TensorId) -> Option<&Tensor> {
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self.0.get(&id)
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}
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pub fn get(&self, tensor: &Tensor) -> Option<&Tensor> {
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self.0.get(&tensor.id())
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}
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pub fn remove(&mut self, tensor: &Tensor) -> Option<Tensor> {
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self.0.remove(&tensor.id())
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}
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pub fn insert(&mut self, tensor: &Tensor, grad: Tensor) -> Option<Tensor> {
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self.0.insert(tensor.id(), grad)
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}
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fn or_insert(&mut self, tensor: &Tensor) -> Result<&mut Tensor> {
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use std::collections::hash_map::Entry;
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let grad = match self.0.entry(tensor.id()) {
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Entry::Occupied(entry) => entry.into_mut(),
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Entry::Vacant(entry) => {
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let grad = tensor.zeros_like()?;
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entry.insert(grad)
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}
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};
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Ok(grad)
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}
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}
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@ -1,3 +1,4 @@
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mod backprop;
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mod cpu_backend;
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#[cfg(feature = "cuda")]
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mod cuda_backend;
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295
src/tensor.rs
295
src/tensor.rs
@ -1,5 +1,4 @@
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use crate::{op::Op, storage::Storage, DType, Device, Error, Result, Shape};
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use std::collections::HashMap;
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use std::sync::Arc;
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/// Unique identifier for tensors.
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@ -541,6 +540,14 @@ impl Tensor {
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self.id
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}
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pub fn is_variable(&self) -> bool {
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self.is_variable
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}
|
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pub(crate) fn op(&self) -> &Option<Op> {
|
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&self.op
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}
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/// Returns a tensor that is a transposed version of the input, the two last dimensions of the
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/// input are swapped.
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pub fn t(&self) -> Result<Tensor> {
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@ -819,256 +826,6 @@ impl Tensor {
|
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}
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Ok(from_storage(storage, shape, op, false))
|
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}
|
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|
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/// Return all the nodes that lead to this value in a topologically sorted vec, the first
|
||||
/// elements having dependencies on the latter ones, e.g. the first element if any is the
|
||||
/// argument.
|
||||
/// This assumes that the op graph is a DAG.
|
||||
fn sorted_nodes(&self) -> Vec<&Tensor> {
|
||||
// The vec of sorted nodes is passed as an owned value rather than a mutable reference
|
||||
// to get around some lifetime limitations.
|
||||
fn walk<'a>(
|
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node: &'a Tensor,
|
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nodes: Vec<&'a Tensor>,
|
||||
already_seen: &mut HashMap<TensorId, bool>,
|
||||
) -> (bool, Vec<&'a Tensor>) {
|
||||
if let Some(&tg) = already_seen.get(&node.id) {
|
||||
return (tg, nodes);
|
||||
}
|
||||
let mut track_grad = false;
|
||||
let mut nodes = if node.is_variable {
|
||||
// Do not call recursively on the "leaf" nodes.
|
||||
track_grad = true;
|
||||
nodes
|
||||
} else if let Some(op) = &node.op {
|
||||
match op {
|
||||
Op::Add(lhs, rhs)
|
||||
| Op::Mul(lhs, rhs)
|
||||
| Op::Sub(lhs, rhs)
|
||||
| Op::Div(lhs, rhs)
|
||||
| Op::BroadcastAdd(lhs, rhs)
|
||||
| Op::BroadcastMul(lhs, rhs)
|
||||
| Op::BroadcastSub(lhs, rhs)
|
||||
| Op::BroadcastDiv(lhs, rhs)
|
||||
| Op::Embedding(lhs, rhs)
|
||||
| Op::Matmul(lhs, rhs) => {
|
||||
let (tg, nodes) = walk(lhs, nodes, already_seen);
|
||||
track_grad |= tg;
|
||||
let (tg, nodes) = walk(rhs, nodes, already_seen);
|
||||
track_grad |= tg;
|
||||
nodes
|
||||
}
|
||||
Op::Cat(args, _) => args.iter().fold(nodes, |nodes, arg| {
|
||||
let (tg, nodes) = walk(arg, nodes, already_seen);
|
||||
track_grad |= tg;
|
||||
nodes
|
||||
}),
|
||||
Op::Affine { arg, mul, .. } => {
|
||||
if *mul == 0. {
|
||||
nodes
|
||||
} else {
|
||||
let (tg, nodes) = walk(arg, nodes, already_seen);
|
||||
track_grad |= tg;
|
||||
nodes
|
||||
}
|
||||
}
|
||||
Op::Reshape(node)
|
||||
| Op::Broadcast(node)
|
||||
| Op::ToDType(node)
|
||||
| Op::ToDevice(node)
|
||||
| Op::Transpose(node, _, _)
|
||||
| Op::Narrow(node, _, _, _)
|
||||
| Op::Softmax(node, _)
|
||||
| Op::Sqr(node)
|
||||
| Op::Sqrt(node)
|
||||
| Op::Gelu(node)
|
||||
| Op::Exp(node)
|
||||
| Op::Log(node)
|
||||
| Op::Sin(node)
|
||||
| Op::Cos(node)
|
||||
| Op::Abs(node)
|
||||
| Op::Neg(node) => {
|
||||
let (tg, nodes) = walk(node, nodes, already_seen);
|
||||
track_grad |= tg;
|
||||
nodes
|
||||
}
|
||||
}
|
||||
} else {
|
||||
nodes
|
||||
};
|
||||
already_seen.insert(node.id, track_grad);
|
||||
if track_grad {
|
||||
nodes.push(node);
|
||||
}
|
||||
(track_grad, nodes)
|
||||
}
|
||||
let (_tg, mut nodes) = walk(self, vec![], &mut HashMap::new());
|
||||
nodes.reverse();
|
||||
nodes
|
||||
}
|
||||
|
||||
pub fn backward(&self) -> Result<GradStore> {
|
||||
let sorted_nodes = self.sorted_nodes();
|
||||
let mut grads = GradStore::new();
|
||||
grads.insert(self, self.ones_like()?);
|
||||
for node in sorted_nodes.iter() {
|
||||
if node.is_variable {
|
||||
continue;
|
||||
}
|
||||
let grad = grads.remove(node).unwrap();
|
||||
// TODO: We should perform all these operations in place (or at least not track the
|
||||
// whole graph).
|
||||
// The only drawback would be if we wanted to support grad of grad but this is out of
|
||||
// scope.
|
||||
if let Some(op) = &node.op {
|
||||
match op {
|
||||
Op::Add(lhs, rhs) => {
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.add(&grad)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.add(&grad)?;
|
||||
}
|
||||
Op::Sub(lhs, rhs) => {
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.add(&grad)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.sub(&grad)?;
|
||||
}
|
||||
Op::Mul(lhs, rhs) => {
|
||||
let lhs_grad = grad.mul(rhs)?;
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
|
||||
let rhs_grad = grad.mul(lhs)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
|
||||
}
|
||||
Op::Div(lhs, rhs) => {
|
||||
let lhs_grad = grad.div(rhs)?;
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
|
||||
let rhs_grad = grad.mul(lhs)?.div(&rhs.sqr()?)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
|
||||
}
|
||||
Op::BroadcastAdd(lhs, rhs) => {
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.broadcast_add(&grad)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.broadcast_add(&grad)?;
|
||||
}
|
||||
Op::BroadcastSub(lhs, rhs) => {
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.broadcast_add(&grad)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.broadcast_sub(&grad)?;
|
||||
}
|
||||
Op::BroadcastMul(lhs, rhs) => {
|
||||
let lhs_grad = grad.broadcast_mul(rhs)?;
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.broadcast_add(&lhs_grad)?;
|
||||
let rhs_grad = grad.broadcast_mul(lhs)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.broadcast_add(&rhs_grad)?;
|
||||
}
|
||||
Op::BroadcastDiv(lhs, rhs) => {
|
||||
let lhs_grad = grad.broadcast_div(rhs)?;
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.broadcast_add(&lhs_grad)?;
|
||||
let rhs_grad = grad.broadcast_mul(lhs)?.broadcast_div(&rhs.sqr()?)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.broadcast_add(&rhs_grad)?;
|
||||
}
|
||||
Op::Embedding(_lhs, _rhs) => {
|
||||
return Err(Error::BackwardNotSupported { op: "embedding" })
|
||||
}
|
||||
Op::Matmul(lhs, rhs) => {
|
||||
// Skipping checks, the op went ok, we can skip
|
||||
// the matmul size checks for now.
|
||||
|
||||
let lhs_grad = grad.matmul(&rhs.t()?)?;
|
||||
let lhs_sum_grad = grads.or_insert(lhs)?;
|
||||
*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
|
||||
|
||||
let rhs_grad = lhs.t()?.matmul(&grad)?;
|
||||
let rhs_sum_grad = grads.or_insert(rhs)?;
|
||||
*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
|
||||
}
|
||||
Op::Cat(args, dim) => {
|
||||
let mut start_idx = 0;
|
||||
for arg in args {
|
||||
let len = arg.dims()[*dim];
|
||||
let arg_grad = grad.narrow(*dim, start_idx, len)?;
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&arg_grad)?;
|
||||
start_idx += len;
|
||||
}
|
||||
}
|
||||
Op::Broadcast(_arg) => {
|
||||
return Err(Error::BackwardNotSupported { op: "broadcast" })
|
||||
}
|
||||
Op::ToDType(arg) => {
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&grad.to_dtype(node.dtype())?)?
|
||||
}
|
||||
Op::Affine { arg, mul, .. } => {
|
||||
let arg_grad = grad.affine(*mul, 0.)?;
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&arg_grad)?
|
||||
}
|
||||
Op::Log(arg) => {
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&(&grad * *node)?)?
|
||||
}
|
||||
Op::Sin(arg) => {
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&(&grad * arg.cos())?)?
|
||||
}
|
||||
Op::Cos(arg) => {
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.sub(&(&grad * arg.sin())?)?
|
||||
}
|
||||
Op::Abs(_args) => return Err(Error::BackwardNotSupported { op: "abs" }),
|
||||
Op::Exp(arg) => {
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&(&grad / arg)?)?
|
||||
}
|
||||
Op::Neg(arg) => {
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.sub(&grad)?
|
||||
}
|
||||
Op::Narrow(_arg, _, _, _) => {
|
||||
return Err(Error::BackwardNotSupported { op: "narrow" })
|
||||
}
|
||||
Op::Softmax(_arg, _) => {
|
||||
return Err(Error::BackwardNotSupported { op: "softmax" })
|
||||
}
|
||||
Op::Reshape(_arg) => return Err(Error::BackwardNotSupported { op: "reshape" }),
|
||||
Op::Gelu(_) => return Err(Error::BackwardNotSupported { op: "gelu" }),
|
||||
Op::Sqr(arg) => {
|
||||
let arg_grad = arg.mul(&grad)?.affine(2., 0.)?;
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&arg_grad)?
|
||||
}
|
||||
Op::Sqrt(arg) => {
|
||||
let arg_grad = grad.div(arg)?.affine(0.5, 0.)?;
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&arg_grad)?
|
||||
}
|
||||
Op::ToDevice(arg) => {
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
let arg_grad = grad.to_device(&sum_grad.device())?;
|
||||
*sum_grad = sum_grad.add(&arg_grad)?
|
||||
}
|
||||
Op::Transpose(arg, dim1, dim2) => {
|
||||
let arg_grad = grad.transpose(*dim1, *dim2)?;
|
||||
let sum_grad = grads.or_insert(arg)?;
|
||||
*sum_grad = sum_grad.add(&arg_grad)?
|
||||
}
|
||||
};
|
||||
}
|
||||
}
|
||||
Ok(grads)
|
||||
}
|
||||
}
|
||||
|
||||
macro_rules! bin_trait {
|
||||
@ -1127,39 +884,3 @@ bin_trait!(Add, add, |_| 1., |v| v);
|
||||
bin_trait!(Sub, sub, |_| 1., |v: f64| -v);
|
||||
bin_trait!(Mul, mul, |v| v, |_| 0.);
|
||||
bin_trait!(Div, div, |v| 1. / v, |_| 0.);
|
||||
|
||||
pub struct GradStore(HashMap<TensorId, Tensor>);
|
||||
|
||||
impl GradStore {
|
||||
fn new() -> Self {
|
||||
GradStore(HashMap::new())
|
||||
}
|
||||
|
||||
pub fn get_id(&self, id: TensorId) -> Option<&Tensor> {
|
||||
self.0.get(&id)
|
||||
}
|
||||
|
||||
pub fn get(&self, tensor: &Tensor) -> Option<&Tensor> {
|
||||
self.0.get(&tensor.id)
|
||||
}
|
||||
|
||||
pub fn remove(&mut self, tensor: &Tensor) -> Option<Tensor> {
|
||||
self.0.remove(&tensor.id)
|
||||
}
|
||||
|
||||
pub fn insert(&mut self, tensor: &Tensor, grad: Tensor) -> Option<Tensor> {
|
||||
self.0.insert(tensor.id, grad)
|
||||
}
|
||||
|
||||
fn or_insert(&mut self, tensor: &Tensor) -> Result<&mut Tensor> {
|
||||
use std::collections::hash_map::Entry;
|
||||
let grad = match self.0.entry(tensor.id) {
|
||||
Entry::Occupied(entry) => entry.into_mut(),
|
||||
Entry::Vacant(entry) => {
|
||||
let grad = tensor.zeros_like()?;
|
||||
entry.insert(grad)
|
||||
}
|
||||
};
|
||||
Ok(grad)
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user