mirror of
https://github.com/huggingface/candle.git
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52c5d8c087
* Start adding gather. * Gather cpu implementation + use in simple training. * Add scatter_add for the gradient of gather. * Simple cpu implementation of scatter_add. * Use gather in the simple-training backprop.
411 lines
19 KiB
Rust
411 lines
19 KiB
Rust
use crate::op::{BinaryOp, Op, ReduceOp, UnaryOp};
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use crate::{Error, Result, Tensor, TensorId};
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use std::collections::HashMap;
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// arg has been reduced to node via reduce_dims, expand it back to arg.
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// This has to handle keepdims.
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fn broadcast_back(arg: &Tensor, node: &Tensor, reduced_dims: &[usize]) -> Result<Tensor> {
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if arg.rank() == node.rank() {
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// keepdim = true
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node.broadcast_as(arg.shape())
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} else {
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// keepdim = false
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// first expand the reduced dims.
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node.reshape(reduced_dims)?.broadcast_as(arg.shape())
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}
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}
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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::IndexAdd(t1, t2, t3, _)
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| Op::ScatterAdd(t1, t2, t3, _)
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| Op::CustomOp3(t1, t2, t3, _)
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| Op::WhereCond(t1, t2, t3) => {
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let (tg, nodes) = walk(t1, nodes, already_seen);
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track_grad |= tg;
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let (tg, nodes) = walk(t2, nodes, already_seen);
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track_grad |= tg;
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let (tg, nodes) = walk(t3, nodes, already_seen);
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track_grad |= tg;
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nodes
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}
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Op::Conv1D {
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arg: lhs,
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kernel: rhs,
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..
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}
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| Op::CustomOp2(lhs, rhs, _)
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| Op::Binary(lhs, rhs, _)
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| Op::Gather(lhs, rhs, _)
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| Op::IndexSelect(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::Cmp(node, _)
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| Op::Reduce(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::Unary(node, _)
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| Op::Elu(node, _)
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| Op::CustomOp1(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()?.contiguous()?);
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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). The only drawback would be if we wanted to support grad of grad but
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// this is out of scope.
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if let Some(op) = node.op() {
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match op {
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Op::Binary(lhs, rhs, BinaryOp::Add) => {
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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::Binary(lhs, rhs, BinaryOp::Sub) => {
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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::Binary(lhs, rhs, BinaryOp::Mul) => {
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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::Binary(lhs, rhs, BinaryOp::Div) => {
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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.sub(&rhs_grad)?;
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}
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Op::WhereCond(pred, t, f) => {
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let zeros = grad.zeros_like()?;
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let t_sum_grad = grads.or_insert(t)?;
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let t_grad = pred.where_cond(&grad, &zeros)?;
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*t_sum_grad = t_sum_grad.add(&t_grad)?;
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let f_sum_grad = grads.or_insert(f)?;
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let f_grad = pred.where_cond(&zeros, &grad)?;
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*f_sum_grad = f_sum_grad.add(&f_grad)?;
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}
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Op::Conv1D { .. } => Err(Error::BackwardNotSupported { op: "conv1d" })?,
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Op::Gather(arg, indexes, dim) => {
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.scatter_add(indexes, &grad, *dim)?;
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}
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Op::ScatterAdd(..) => Err(Error::BackwardNotSupported { op: "scatter-add" })?,
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Op::IndexAdd { .. } => Err(Error::BackwardNotSupported { op: "index-add" })?,
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Op::IndexSelect(arg, indexes, dim) => {
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.index_add(indexes, &grad, *dim)?;
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}
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Op::Embedding(_lhs, _rhs) => {
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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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let arg_dims = arg.dims();
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let node_dims = node.dims();
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// The number of dims that have been inserted on the left.
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let left_dims = node_dims.len() - arg_dims.len();
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let mut sum_dims: Vec<usize> = (0..left_dims).collect();
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for (dim, (node_dim, arg_dim)) in node_dims[left_dims..]
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.iter()
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.zip(arg_dims.iter())
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.enumerate()
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{
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if node_dim != arg_dim {
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sum_dims.push(dim + left_dims)
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}
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}
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let mut arg_grad = grad.sum_keepdim(sum_dims.as_slice())?;
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for _i in 0..left_dims {
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arg_grad = arg_grad.squeeze(0)?
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}
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&arg_grad.broadcast_as(sum_grad.dims())?)?;
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}
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Op::Reduce(arg, ReduceOp::Sum, reduced_dims) => {
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let grad = broadcast_back(arg, &grad, reduced_dims)?;
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&grad)?;
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}
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Op::Cmp(_args, _) => return Err(Error::BackwardNotSupported { op: "cmp" }),
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Op::Reduce(arg, ReduceOp::Max, reduced_dims) => {
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let node = broadcast_back(arg, node, reduced_dims)?;
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let grad = broadcast_back(arg, &grad, reduced_dims)?;
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let grad = node.eq(arg)?.to_dtype(grad.dtype())?.mul(&grad)?;
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&grad.broadcast_as(sum_grad.dims())?)?;
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}
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Op::Reduce(arg, ReduceOp::Min, reduced_dims) => {
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let node = broadcast_back(arg, node, reduced_dims)?;
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let grad = broadcast_back(arg, &grad, reduced_dims)?;
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let grad = node.eq(arg)?.to_dtype(grad.dtype())?.mul(&grad)?;
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let sum_grad = grads.or_insert(arg)?;
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*sum_grad = sum_grad.add(&grad.broadcast_as(sum_grad.dims())?)?;
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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::Unary(arg, UnaryOp::Log) => {
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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::Unary(arg, UnaryOp::Sin) => {
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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::Unary(arg, UnaryOp::Cos) => {
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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::Unary(_, UnaryOp::Abs) => Err(Error::BackwardNotSupported { op: "abs" })?,
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Op::Unary(arg, UnaryOp::Exp) => {
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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::Unary(arg, UnaryOp::Neg) => {
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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(ref arg, dim, start_idx, len) => {
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let arg_dims = arg.dims();
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let left_pad = if start_idx == 0 {
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None
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} else {
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let mut dims = arg_dims.to_vec();
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dims[dim] = start_idx;
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Some(Tensor::zeros(dims, grad.dtype(), grad.device())?)
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};
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let right_pad = arg_dims[dim] - start_idx - len;
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let right_pad = if right_pad == 0 {
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None
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} else {
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let mut dims = arg_dims.to_vec();
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dims[dim] = right_pad;
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Some(Tensor::zeros(dims, grad.dtype(), grad.device())?)
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};
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let arg_grad = match (left_pad, right_pad) {
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(None, None) => grad,
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(Some(l), None) => Tensor::cat(&[&l, &grad], dim)?,
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(None, Some(r)) => Tensor::cat(&[&grad, &r], dim)?,
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(Some(l), Some(r)) => Tensor::cat(&[&l, &grad, &r], dim)?,
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};
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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::Reduce(_, ReduceOp::ArgMin, _) => {
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Err(Error::BackwardNotSupported { op: "argmin" })?
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}
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Op::Reduce(_, ReduceOp::ArgMax, _) => {
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Err(Error::BackwardNotSupported { op: "argmax" })?
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}
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Op::Softmax(_arg, _) => Err(Error::BackwardNotSupported { op: "softmax" })?,
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Op::Reshape(arg) => {
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let arg_grad = grad.reshape(arg.dims())?;
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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::Unary(_, UnaryOp::Gelu) => Err(Error::BackwardNotSupported { op: "gelu" })?,
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Op::Unary(_, UnaryOp::Relu) => Err(Error::BackwardNotSupported { op: "relu" })?,
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Op::Elu(..) => Err(Error::BackwardNotSupported { op: "elu" })?,
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Op::CustomOp1(arg, c) => {
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if let Some(arg_grad) = c.bwd(arg, node, &grad)? {
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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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Op::CustomOp2(arg1, arg2, c) => {
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let (arg_grad1, arg_grad2) = c.bwd(arg1, arg2, node, &grad)?;
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if let Some(arg_grad1) = arg_grad1 {
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let sum_grad = grads.or_insert(arg1)?;
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*sum_grad = sum_grad.add(&arg_grad1)?
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}
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if let Some(arg_grad2) = arg_grad2 {
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let sum_grad = grads.or_insert(arg2)?;
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*sum_grad = sum_grad.add(&arg_grad2)?
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}
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}
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Op::CustomOp3(arg1, arg2, arg3, c) => {
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let (arg_grad1, arg_grad2, arg_grad3) =
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c.bwd(arg1, arg2, arg3, node, &grad)?;
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if let Some(arg_grad1) = arg_grad1 {
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let sum_grad = grads.or_insert(arg1)?;
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*sum_grad = sum_grad.add(&arg_grad1)?
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}
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if let Some(arg_grad2) = arg_grad2 {
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let sum_grad = grads.or_insert(arg2)?;
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*sum_grad = sum_grad.add(&arg_grad2)?
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}
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if let Some(arg_grad3) = arg_grad3 {
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let sum_grad = grads.or_insert(arg3)?;
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*sum_grad = sum_grad.add(&arg_grad3)?
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}
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}
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Op::Unary(arg, UnaryOp::Sqr) => {
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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::Unary(arg, UnaryOp::Sqrt) => {
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let arg_grad = grad.div(node)?.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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