Rework the embeddings so that it works on non-contiguous weights + factor out some code.

examples/llama/main.rs

@@ -350,7 +350,8 @@ impl Llama {
     }
 
     fn forward(&self, x: &Tensor, freqs_cis: &Tensor) -> Result<Tensor> {
-        let (_, t) = x.shape().r2()?;
+        // TODO: Support for mini-batches? (i.e. r2)
+        let t = x.shape().r1()?;
         let mut x = self.wte.forward(x)?;
         for block in self.blocks.iter() {
             x = block.forward(&x, freqs_cis)?;
@@ -427,7 +428,7 @@ fn main() -> Result<()> {
     let mut rng = thread_rng();
     for index in 0..args.sample_len {
         let ctxt = &tokens[tokens.len().saturating_sub(CONTEXT_SIZE)..];
-        let input = Tensor::new(ctxt, &Device::Cpu)?.reshape((1, ctxt.len()))?;
+        let input = Tensor::new(ctxt, &Device::Cpu)?;
         let logits = llama.forward(&input, &freqs_cis)?;
         let prs = (&logits / args.temperature)?.softmax(logits.rank() - 1)?;
         let logits_v: Vec<f32> = prs.to_vec1()?;

src/cpu_backend.rs

@@ -88,6 +88,30 @@ fn binary_map<T: Copy, F: FnMut(T, T) -> T>(
     }
 }
 
+fn take<T: Copy>(
+    ids: &[u32],
+    shape: &Shape,
+    stride: &[usize],
+    vs: &[T],
+    vocab_size: usize,
+    hidden_size: usize,
+) -> Result<Vec<T>> {
+    let mut values = Vec::with_capacity(shape.elem_count() * hidden_size);
+    for index in StridedIndex::new(shape.dims(), stride) {
+        let index = ids[index].try_into()?;
+        if index >= vocab_size {
+            return Err(Error::InvalidIndex {
+                index,
+                vocab_size,
+                op: "take",
+            });
+        } else {
+            values.extend(&vs[hidden_size * index..hidden_size * (index + 1)]);
+        }
+    }
+    Ok(values)
+}
+
 fn copy_strided_src_<T: Copy + std::fmt::Display>(
     src: &[T],
     dst: &mut [T],
@@ -380,52 +404,30 @@ impl CpuStorage {
 
     pub(crate) fn embedding_impl(
         &self,
-        rhs: &Self,
+        shape: &Shape,
+        stride: &[usize],
+        vs: &Self,
         hidden_size: usize,
         vocab_size: usize,
     ) -> Result<Self> {
         match self {
-            CpuStorage::U32(lhs) => match rhs {
-                CpuStorage::F32(rhs) => {
-                    let mut weights = Vec::with_capacity(lhs.len() * hidden_size);
-                    for &index in lhs {
-                        let index: usize = index.try_into()?;
-                        if index >= vocab_size {
-                            return Err(Error::InvalidIndex {
-                                index,
-                                vocab_size,
-                                op: "embedding",
-                            });
-                        } else {
-                            weights.extend(&rhs[hidden_size * index..hidden_size * (index + 1)]);
+            CpuStorage::U32(ids) => match vs {
+                CpuStorage::F32(vs) => {
+                    let storage = take(ids, shape, stride, vs, vocab_size, hidden_size)?;
+                    Ok(CpuStorage::F32(storage))
                 }
+                CpuStorage::F64(vs) => {
+                    let storage = take(ids, shape, stride, vs, vocab_size, hidden_size)?;
+                    Ok(CpuStorage::F64(storage))
                 }
-                    Ok(CpuStorage::F32(weights))
+                CpuStorage::U32(vs) => {
+                    let storage = take(ids, shape, stride, vs, vocab_size, hidden_size)?;
+                    Ok(CpuStorage::U32(storage))
                 }
-                CpuStorage::F64(rhs) => {
-                    let mut weights = Vec::with_capacity(lhs.len() * hidden_size);
-                    for &index in lhs {
-                        let index: usize = index.try_into()?;
-                        if index >= vocab_size {
-                            return Err(Error::InvalidIndex {
-                                index,
-                                vocab_size,
-                                op: "embedding",
-                            });
-                        } else {
-                            weights.extend(&rhs[hidden_size * index..hidden_size * (index + 1)]);
-                        }
-                    }
-                    Ok(CpuStorage::F64(weights))
-                }
-                rhs => Err(Error::UnexpectedDType {
-                    expected: DType::F32,
-                    got: rhs.dtype(),
-                }),
             },
-            lhs => Err(Error::UnexpectedDType {
+            ids => Err(Error::UnexpectedDType {
                 expected: DType::U32,
-                got: lhs.dtype(),
+                got: ids.dtype(),
             }),
         }
     }

src/cuda_backend.rs

@@ -413,6 +413,8 @@ impl CudaStorage {
 
     pub(crate) fn embedding_impl(
         &self,
+        _shape: &Shape,
+        _stride: &[usize],
         _rhs: &Self,
         _hidden_size: usize,
         _vocab_size: usize,

src/dummy_cuda_backend.rs

@@ -90,7 +90,14 @@ impl CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
-    pub(crate) fn embedding_impl(&self, _: &Self, _: usize, _: usize) -> Result<Self> {
+    pub(crate) fn embedding_impl(
+        &self,
+        _: &Shape,
+        _: &[usize],
+        _: &Self,
+        _: usize,
+        _: usize,
+    ) -> Result<Self> {
         Err(Error::NotCompiledWithCudaSupport)
     }
 

src/storage.rs

@@ -156,19 +156,20 @@ impl Storage {
 
     pub(crate) fn embedding_impl(
         &self,
+        shape: &Shape,
+        stride: &[usize],
         rhs: &Self,
         hidden_size: usize,
         vocab_size: usize,
     ) -> Result<Self> {
         self.same_device(rhs, "embedding")?;
-        self.same_dtype(rhs, "embedding")?;
         match (self, rhs) {
             (Storage::Cpu(lhs), Storage::Cpu(rhs)) => {
-                let storage = lhs.embedding_impl(rhs, hidden_size, vocab_size)?;
+                let storage = lhs.embedding_impl(shape, stride, rhs, hidden_size, vocab_size)?;
                 Ok(Self::Cpu(storage))
             }
             (Self::Cuda(lhs), Self::Cuda(rhs)) => {
-                let storage = lhs.embedding_impl(rhs, hidden_size, vocab_size)?;
+                let storage = lhs.embedding_impl(shape, stride, rhs, hidden_size, vocab_size)?;
                 Ok(Self::Cuda(storage))
             }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {

src/tensor.rs

@@ -435,11 +435,16 @@ impl Tensor {
                 op: "embedding",
             });
         }
-        let seq_len = ids.shape().r1()?;
+        let ids_shape = ids.shape();
+        let seq_len = ids_shape.r1()?;
         let (vocab_size, hidden_size) = rhs.shape().r2()?;
-        let storage = ids
-            .storage
-            .embedding_impl(&rhs.storage, hidden_size, vocab_size)?;
+        let storage = ids.storage.embedding_impl(
+            ids_shape,
+            &ids.stride,
+            &rhs.storage,
+            hidden_size,
+            vocab_size,
+        )?;
         let shape: Shape = (seq_len, hidden_size).into();
         let op = if ids.track_op() || rhs.track_op() {
             Some(Op::Embedding(ids.clone(), rhs.clone()))